In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import librosa
import tensorflow as tf
import os
from tqdm import tqdm
import wave
import contextlib
import cv2
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras import datasets, layers, models
from sklearn import metrics
from sklearn.metrics import r2_score, confusion_matrix
import IPython.display as ipd
import random

2024-01-21 14:22:28.917033: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
In [2]:
click = 1
In [3]:
try:
    X = np.load("X_t.npy")
    Y = np.load("Y_t.npy")
except:
    click = 0
In [4]:
os.listdir("Training_Files")
Out[4]:
['.DS_Store',
 'Beatles',
 'Led Zeppelin',
 'Nirvana',
 'Pink Floyd',
 'Elvis Presley',
 'Beach Boys',
 'Queen',
 'ABBA',
 'Bob Dylan']
In [5]:
def features_extractor(file_name):
    audio, sample_rate = librosa.load(file_name)
    mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=60)
    mfccs_scaled_features = np.mean(mfccs_features.T,axis=0)

    return mfccs_scaled_features
In [6]:
len(features_extractor("/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/ISawHerStandingThereRemastered2009.wav"))
Out[6]:
60
In [7]:
y1, sr1 = librosa.load("/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/ISawHerStandingThereRemastered2009.wav")
In [8]:
librosa.feature.mfcc(y = y1, sr = sr1, n_mfcc = 40).shape
Out[8]:
(40, 7493)
In [9]:
y2, sr2 = y2, sr2 = librosa.load("/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/WhileMyGuitarGentlyWeepsRemastered2009.wav")
In [10]:
librosa.feature.mfcc(y = y2, sr = sr2, n_mfcc = 40).shape
Out[10]:
(40, 12275)
In [11]:
len(librosa.feature.mfcc(y = y2, sr = sr2, n_mfcc = 40)[0])
Out[11]:
12275
In [12]:
fname = '/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/WhileMyGuitarGentlyWeepsRemastered2009.wav'
with contextlib.closing(wave.open(fname,'r')) as f:
    frames = f.getnframes()
    rate = f.getframerate()
    duration = frames / rate
    print((duration))

285.00172335600905
In [13]:
def duration(filename):
    with contextlib.closing(wave.open(filename,'r')) as f:
        frames = f.getnframes()
        rate = f.getframerate()
        duration = frames / rate
        return int(duration)
In [14]:
def near_empty_array(x, n):
    y = [x]
    for i in range(0, n - 1):
        y.append(0)
    return y

near_empty_array(5, 4)
Out[14]:
[5, 0, 0, 0]
In [15]:
np.append(features_extractor(fname), 20).tolist()
Out[15]:
[-90.9020767211914,
 90.09610748291016,
 -22.818819046020508,
 40.834877014160156,
 3.6339235305786133,
 10.50902271270752,
 -5.8387346267700195,
 8.07880687713623,
 0.3058115541934967,
 -0.19161468744277954,
 -4.686412811279297,
 3.4597251415252686,
 -2.014287233352661,
 1.6238023042678833,
 -2.8573644161224365,
 -1.4839766025543213,
 -8.509678840637207,
 1.2135590314865112,
 -9.312557220458984,
 -6.9956865310668945,
 -4.266140937805176,
 -1.599496603012085,
 -6.903672218322754,
 -3.521676778793335,
 -8.436915397644043,
 -0.20250433683395386,
 -6.002135753631592,
 0.37704238295555115,
 -5.920943737030029,
 -5.659139633178711,
 -1.898746132850647,
 2.9211552143096924,
 -3.8918182849884033,
 -3.6990182399749756,
 -5.4410271644592285,
 -1.9968183040618896,
 -6.6689677238464355,
 -0.46719056367874146,
 -2.307814836502075,
 -1.504570722579956,
 -3.611607789993286,
 1.1021623611450195,
 -2.398678779602051,
 -0.9391394257545471,
 -2.6728131771087646,
 -1.6004353761672974,
 -5.713006019592285,
 2.2308390140533447,
 -0.4296121895313263,
 -4.351404666900635,
 -3.5961544513702393,
 3.0389599800109863,
 1.7495242357254028,
 -0.9395294189453125,
 -3.709394693374634,
 -1.320820689201355,
 -5.4441094398498535,
 -4.134355068206787,
 -3.3126020431518555,
 -0.559026837348938,
 20.0]
In [16]:
if click == 0:
    X = []
    Y = []
In [17]:
if click == 0:
    for x in os.listdir("Training_Files"):
        if x != ".DS_Store":
            k = os.listdir("Training_Files/" + x)
            for j in tqdm(range(0, len(k))):
                if k[j] != ".DS_Store":
                    mfcc = features_extractor("Training_Files/" + x + "/" + k[j])
                    time = duration("Training_Files/" + x + "/" + k[j])
                    mfcc = np.append(mfcc, time)
                    X.append(mfcc.tolist())
                    Y.append(x)
In [18]:
if click == 0:
    X = np.array(X)
    Y = np.array(Y)
    np.save("X_t.npy", X)
    np.save("Y_t.npy", Y)
In [19]:
np.unique(Y)
Out[19]:
array(['ABBA', 'Beach Boys', 'Beatles', 'Bob Dylan', 'Elvis Presley',
       'Led Zeppelin', 'Nirvana', 'Pink Floyd', 'Queen'], dtype='<U13')
In [20]:
l = LabelEncoder()
l.fit(Y)
Y_t = l.transform(Y)
In [21]:
Y_t
Out[21]:
array([2, 2, 2, ..., 3, 3, 3])
In [22]:
X_train, X_test, Y_train, Y_test = train_test_split(X, Y_t, test_size=0.40)
In [23]:
X_train.shape
Out[23]:
(843, 61)
In [24]:
# model initialization
model = tf.keras.Sequential()

# adding the 1st and 2nd layer layer
model.add(tf.keras.layers.Flatten(input_shape=(61,)))
model.add(tf.keras.layers.Dense(256, activation = 'relu'))
model.add(tf.keras.layers.Dense(128, activation = 'relu'))
#__add__ additional Intermediate Dense layers here to create the output
model.add(tf.keras.layers.Dense(84, activation = 'leaky_relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(56, activation = 'relu'))
model.add(tf.keras.layers.Dense(42, activation = 'relu'))
#model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.Dense(36, activation = 'relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(24, activation = 'relu'))
model.add(tf.keras.layers.Dense(18, activation = 'relu'))
#model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(15, activation = 'relu'))
#model.add(tf.keras.layers.Dropout(0.2))
#__output__layer with correct output shape and activation function[google if finding this difficult to get]
model.add(tf.keras.layers.Dense(9, activation = 'softmax'))
#model.summary()
In [25]:
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 flatten (Flatten)           (None, 61)                0         
                                                                 
 dense (Dense)               (None, 256)               15872     
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 84)                10836     
                                                                 
 dropout (Dropout)           (None, 84)                0         
                                                                 
 dense_3 (Dense)             (None, 56)                4760      
                                                                 
 dense_4 (Dense)             (None, 42)                2394      
                                                                 
 dense_5 (Dense)             (None, 36)                1548      
                                                                 
 dropout_1 (Dropout)         (None, 36)                0         
                                                                 
 dense_6 (Dense)             (None, 24)                888       
                                                                 
 dense_7 (Dense)             (None, 18)                450       
                                                                 
 dense_8 (Dense)             (None, 15)                285       
                                                                 
 dense_9 (Dense)             (None, 9)                 144       
                                                                 
=================================================================
Total params: 70,073
Trainable params: 70,073
Non-trainable params: 0
_________________________________________________________________
In [26]:
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 1e-3),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

history = model.fit(X_train, Y_train, epochs=750, 
                    validation_data=(X_test, Y_test))

Epoch 1/750

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/keras/backend.py:5612: UserWarning: "`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?
  output, from_logits = _get_logits(

27/27 [==============================] - 2s 9ms/step - loss: 2.2309 - accuracy: 0.1684 - val_loss: 2.0783 - val_accuracy: 0.2349
Epoch 2/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0192 - accuracy: 0.2467 - val_loss: 1.9579 - val_accuracy: 0.2278
Epoch 3/750
27/27 [==============================] - 0s 3ms/step - loss: 1.9000 - accuracy: 0.2835 - val_loss: 1.8655 - val_accuracy: 0.3363
Epoch 4/750
27/27 [==============================] - 0s 2ms/step - loss: 1.8117 - accuracy: 0.3310 - val_loss: 1.8435 - val_accuracy: 0.3363
Epoch 5/750
27/27 [==============================] - 0s 2ms/step - loss: 1.7488 - accuracy: 0.3796 - val_loss: 1.7757 - val_accuracy: 0.3452
Epoch 6/750
27/27 [==============================] - 0s 3ms/step - loss: 1.7170 - accuracy: 0.3938 - val_loss: 1.7661 - val_accuracy: 0.3559
Epoch 7/750
27/27 [==============================] - 0s 2ms/step - loss: 1.6410 - accuracy: 0.4164 - val_loss: 1.6688 - val_accuracy: 0.3968
Epoch 8/750
27/27 [==============================] - 0s 2ms/step - loss: 1.5512 - accuracy: 0.4579 - val_loss: 1.6331 - val_accuracy: 0.4039
Epoch 9/750
27/27 [==============================] - 0s 3ms/step - loss: 1.5217 - accuracy: 0.4520 - val_loss: 1.6295 - val_accuracy: 0.3932
Epoch 10/750
27/27 [==============================] - 0s 2ms/step - loss: 1.4840 - accuracy: 0.4662 - val_loss: 1.6166 - val_accuracy: 0.4021
Epoch 11/750
27/27 [==============================] - 0s 3ms/step - loss: 1.4681 - accuracy: 0.4603 - val_loss: 1.7658 - val_accuracy: 0.3665
Epoch 12/750
27/27 [==============================] - 0s 2ms/step - loss: 1.5047 - accuracy: 0.4591 - val_loss: 1.4927 - val_accuracy: 0.4395
Epoch 13/750
27/27 [==============================] - 0s 2ms/step - loss: 1.3604 - accuracy: 0.5208 - val_loss: 1.5547 - val_accuracy: 0.4342
Epoch 14/750
27/27 [==============================] - 0s 2ms/step - loss: 1.3485 - accuracy: 0.5089 - val_loss: 1.5514 - val_accuracy: 0.4520
Epoch 15/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2896 - accuracy: 0.5255 - val_loss: 1.4593 - val_accuracy: 0.4609
Epoch 16/750
27/27 [==============================] - 0s 2ms/step - loss: 1.3032 - accuracy: 0.5196 - val_loss: 1.4924 - val_accuracy: 0.4733
Epoch 17/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2702 - accuracy: 0.5350 - val_loss: 1.6040 - val_accuracy: 0.4431
Epoch 18/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2555 - accuracy: 0.5326 - val_loss: 1.4062 - val_accuracy: 0.4786
Epoch 19/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2342 - accuracy: 0.5421 - val_loss: 1.4464 - val_accuracy: 0.4804
Epoch 20/750
27/27 [==============================] - 0s 2ms/step - loss: 1.1799 - accuracy: 0.5635 - val_loss: 1.4213 - val_accuracy: 0.4840
Epoch 21/750
27/27 [==============================] - 0s 2ms/step - loss: 1.1299 - accuracy: 0.5741 - val_loss: 1.3660 - val_accuracy: 0.4964
Epoch 22/750
27/27 [==============================] - 0s 2ms/step - loss: 1.1253 - accuracy: 0.5813 - val_loss: 1.4517 - val_accuracy: 0.5107
Epoch 23/750
27/27 [==============================] - 0s 2ms/step - loss: 1.1352 - accuracy: 0.5801 - val_loss: 1.4085 - val_accuracy: 0.5089
Epoch 24/750
27/27 [==============================] - 0s 2ms/step - loss: 1.1189 - accuracy: 0.5848 - val_loss: 1.4072 - val_accuracy: 0.5089
Epoch 25/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0908 - accuracy: 0.5907 - val_loss: 1.4463 - val_accuracy: 0.4769
Epoch 26/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0607 - accuracy: 0.6002 - val_loss: 1.2595 - val_accuracy: 0.5730
Epoch 27/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0316 - accuracy: 0.6311 - val_loss: 1.3228 - val_accuracy: 0.5498
Epoch 28/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0194 - accuracy: 0.6192 - val_loss: 1.2565 - val_accuracy: 0.5694
Epoch 29/750
27/27 [==============================] - 0s 3ms/step - loss: 0.9904 - accuracy: 0.6168 - val_loss: 1.2872 - val_accuracy: 0.5836
Epoch 30/750
27/27 [==============================] - 0s 3ms/step - loss: 0.9879 - accuracy: 0.6287 - val_loss: 1.2841 - val_accuracy: 0.5979
Epoch 31/750
27/27 [==============================] - 0s 2ms/step - loss: 0.9417 - accuracy: 0.6655 - val_loss: 1.2634 - val_accuracy: 0.6210
Epoch 32/750
27/27 [==============================] - 0s 2ms/step - loss: 0.8820 - accuracy: 0.6773 - val_loss: 1.4339 - val_accuracy: 0.5623
Epoch 33/750
27/27 [==============================] - 0s 2ms/step - loss: 0.8699 - accuracy: 0.6916 - val_loss: 1.2147 - val_accuracy: 0.6299
Epoch 34/750
27/27 [==============================] - 0s 2ms/step - loss: 0.8377 - accuracy: 0.6987 - val_loss: 1.3832 - val_accuracy: 0.6032
Epoch 35/750
27/27 [==============================] - 0s 2ms/step - loss: 0.8631 - accuracy: 0.7023 - val_loss: 1.2369 - val_accuracy: 0.6121
Epoch 36/750
27/27 [==============================] - 0s 2ms/step - loss: 0.7568 - accuracy: 0.7343 - val_loss: 1.1901 - val_accuracy: 0.6566
Epoch 37/750
27/27 [==============================] - 0s 2ms/step - loss: 0.7107 - accuracy: 0.7426 - val_loss: 1.3033 - val_accuracy: 0.6192
Epoch 38/750
27/27 [==============================] - 0s 2ms/step - loss: 0.6933 - accuracy: 0.7367 - val_loss: 1.3322 - val_accuracy: 0.6566
Epoch 39/750
27/27 [==============================] - 0s 3ms/step - loss: 0.6862 - accuracy: 0.7521 - val_loss: 1.3402 - val_accuracy: 0.6139
Epoch 40/750
27/27 [==============================] - 0s 2ms/step - loss: 0.7298 - accuracy: 0.7378 - val_loss: 1.3216 - val_accuracy: 0.6548
Epoch 41/750
27/27 [==============================] - 0s 2ms/step - loss: 0.6978 - accuracy: 0.7568 - val_loss: 1.2447 - val_accuracy: 0.6833
Epoch 42/750
27/27 [==============================] - 0s 2ms/step - loss: 0.6072 - accuracy: 0.7675 - val_loss: 1.3619 - val_accuracy: 0.6726
Epoch 43/750
27/27 [==============================] - 0s 2ms/step - loss: 0.6573 - accuracy: 0.7782 - val_loss: 1.3163 - val_accuracy: 0.6423
Epoch 44/750
27/27 [==============================] - 0s 3ms/step - loss: 0.5581 - accuracy: 0.8114 - val_loss: 1.6847 - val_accuracy: 0.6335
Epoch 45/750
27/27 [==============================] - 0s 2ms/step - loss: 0.6017 - accuracy: 0.7865 - val_loss: 1.3643 - val_accuracy: 0.6406
Epoch 46/750
27/27 [==============================] - 0s 2ms/step - loss: 0.5663 - accuracy: 0.7983 - val_loss: 1.5263 - val_accuracy: 0.6530
Epoch 47/750
27/27 [==============================] - 0s 2ms/step - loss: 0.6076 - accuracy: 0.7794 - val_loss: 1.3316 - val_accuracy: 0.6815
Epoch 48/750
27/27 [==============================] - 0s 2ms/step - loss: 0.5112 - accuracy: 0.8102 - val_loss: 1.2632 - val_accuracy: 0.6975
Epoch 49/750
27/27 [==============================] - 0s 2ms/step - loss: 0.5024 - accuracy: 0.8185 - val_loss: 1.4600 - val_accuracy: 0.6690
Epoch 50/750
27/27 [==============================] - 0s 2ms/step - loss: 0.4467 - accuracy: 0.8493 - val_loss: 1.4405 - val_accuracy: 0.6940
Epoch 51/750
27/27 [==============================] - 0s 2ms/step - loss: 0.4166 - accuracy: 0.8529 - val_loss: 1.7584 - val_accuracy: 0.6815
Epoch 52/750
27/27 [==============================] - 0s 2ms/step - loss: 0.4906 - accuracy: 0.8209 - val_loss: 1.3596 - val_accuracy: 0.6762
Epoch 53/750
27/27 [==============================] - 0s 2ms/step - loss: 0.5086 - accuracy: 0.8090 - val_loss: 1.5776 - val_accuracy: 0.6655
Epoch 54/750
27/27 [==============================] - 0s 2ms/step - loss: 0.4682 - accuracy: 0.8458 - val_loss: 1.2807 - val_accuracy: 0.7260
Epoch 55/750
27/27 [==============================] - 0s 2ms/step - loss: 0.4103 - accuracy: 0.8493 - val_loss: 1.3087 - val_accuracy: 0.7171
Epoch 56/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3506 - accuracy: 0.8719 - val_loss: 1.4368 - val_accuracy: 0.7082
Epoch 57/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3682 - accuracy: 0.8648 - val_loss: 1.5397 - val_accuracy: 0.7100
Epoch 58/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3566 - accuracy: 0.8778 - val_loss: 1.4721 - val_accuracy: 0.7367
Epoch 59/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3376 - accuracy: 0.8731 - val_loss: 1.5414 - val_accuracy: 0.6886
Epoch 60/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3127 - accuracy: 0.8921 - val_loss: 1.5571 - val_accuracy: 0.7224
Epoch 61/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3434 - accuracy: 0.8719 - val_loss: 1.6903 - val_accuracy: 0.7242
Epoch 62/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3702 - accuracy: 0.8743 - val_loss: 1.4159 - val_accuracy: 0.6904
Epoch 63/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2903 - accuracy: 0.8909 - val_loss: 1.7148 - val_accuracy: 0.7046
Epoch 64/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2770 - accuracy: 0.9134 - val_loss: 1.8661 - val_accuracy: 0.6922
Epoch 65/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3163 - accuracy: 0.8814 - val_loss: 1.7670 - val_accuracy: 0.6922
Epoch 66/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3042 - accuracy: 0.8849 - val_loss: 1.5981 - val_accuracy: 0.7153
Epoch 67/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2386 - accuracy: 0.9170 - val_loss: 1.5692 - val_accuracy: 0.7331
Epoch 68/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2252 - accuracy: 0.9300 - val_loss: 1.7595 - val_accuracy: 0.6762
Epoch 69/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3219 - accuracy: 0.8992 - val_loss: 1.8673 - val_accuracy: 0.6904
Epoch 70/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3510 - accuracy: 0.8802 - val_loss: 1.6013 - val_accuracy: 0.7171
Epoch 71/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2348 - accuracy: 0.9193 - val_loss: 1.7731 - val_accuracy: 0.6975
Epoch 72/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2129 - accuracy: 0.9253 - val_loss: 1.8862 - val_accuracy: 0.6975
Epoch 73/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2830 - accuracy: 0.9051 - val_loss: 1.7763 - val_accuracy: 0.7064
Epoch 74/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2817 - accuracy: 0.9098 - val_loss: 1.7395 - val_accuracy: 0.7295
Epoch 75/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1856 - accuracy: 0.9348 - val_loss: 1.9753 - val_accuracy: 0.7171
Epoch 76/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2136 - accuracy: 0.9265 - val_loss: 1.6833 - val_accuracy: 0.7313
Epoch 77/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1446 - accuracy: 0.9537 - val_loss: 2.1296 - val_accuracy: 0.7028
Epoch 78/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1937 - accuracy: 0.9265 - val_loss: 1.8009 - val_accuracy: 0.7349
Epoch 79/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1849 - accuracy: 0.9324 - val_loss: 2.1563 - val_accuracy: 0.7349
Epoch 80/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1471 - accuracy: 0.9585 - val_loss: 2.1106 - val_accuracy: 0.6975
Epoch 81/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1348 - accuracy: 0.9597 - val_loss: 2.5593 - val_accuracy: 0.6797
Epoch 82/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1469 - accuracy: 0.9490 - val_loss: 2.1660 - val_accuracy: 0.7153
Epoch 83/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1711 - accuracy: 0.9478 - val_loss: 2.4314 - val_accuracy: 0.6833
Epoch 84/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1403 - accuracy: 0.9490 - val_loss: 2.3948 - val_accuracy: 0.7224
Epoch 85/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1412 - accuracy: 0.9490 - val_loss: 2.6475 - val_accuracy: 0.6868
Epoch 86/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1759 - accuracy: 0.9407 - val_loss: 2.2151 - val_accuracy: 0.6904
Epoch 87/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1400 - accuracy: 0.9514 - val_loss: 2.4839 - val_accuracy: 0.6940
Epoch 88/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1690 - accuracy: 0.9490 - val_loss: 1.9097 - val_accuracy: 0.7367
Epoch 89/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2458 - accuracy: 0.9336 - val_loss: 1.5784 - val_accuracy: 0.7473
Epoch 90/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1467 - accuracy: 0.9573 - val_loss: 1.9773 - val_accuracy: 0.7189
Epoch 91/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1207 - accuracy: 0.9680 - val_loss: 2.2338 - val_accuracy: 0.7100
Epoch 92/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1025 - accuracy: 0.9703 - val_loss: 2.3225 - val_accuracy: 0.6922
Epoch 93/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1358 - accuracy: 0.9573 - val_loss: 2.3724 - val_accuracy: 0.7064
Epoch 94/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1554 - accuracy: 0.9549 - val_loss: 2.4760 - val_accuracy: 0.6744
Epoch 95/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2219 - accuracy: 0.9288 - val_loss: 2.0524 - val_accuracy: 0.7189
Epoch 96/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1302 - accuracy: 0.9573 - val_loss: 2.3122 - val_accuracy: 0.7153
Epoch 97/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0803 - accuracy: 0.9786 - val_loss: 2.5526 - val_accuracy: 0.7117
Epoch 98/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0751 - accuracy: 0.9751 - val_loss: 2.6208 - val_accuracy: 0.7278
Epoch 99/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0498 - accuracy: 0.9893 - val_loss: 2.5974 - val_accuracy: 0.7295
Epoch 100/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0476 - accuracy: 0.9870 - val_loss: 2.5990 - val_accuracy: 0.7260
Epoch 101/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0552 - accuracy: 0.9834 - val_loss: 2.6552 - val_accuracy: 0.7295
Epoch 102/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1570 - accuracy: 0.9668 - val_loss: 2.6024 - val_accuracy: 0.6690
Epoch 103/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1271 - accuracy: 0.9620 - val_loss: 2.2812 - val_accuracy: 0.7206
Epoch 104/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0750 - accuracy: 0.9751 - val_loss: 2.5919 - val_accuracy: 0.6975
Epoch 105/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0912 - accuracy: 0.9751 - val_loss: 2.8756 - val_accuracy: 0.6815
Epoch 106/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3275 - accuracy: 0.9300 - val_loss: 2.3939 - val_accuracy: 0.6690
Epoch 107/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3066 - accuracy: 0.9063 - val_loss: 1.9142 - val_accuracy: 0.6922
Epoch 108/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1935 - accuracy: 0.9537 - val_loss: 2.1139 - val_accuracy: 0.7011
Epoch 109/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1110 - accuracy: 0.9715 - val_loss: 2.0702 - val_accuracy: 0.7189
Epoch 110/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0687 - accuracy: 0.9798 - val_loss: 2.3422 - val_accuracy: 0.7171
Epoch 111/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0417 - accuracy: 0.9881 - val_loss: 2.4723 - val_accuracy: 0.7367
Epoch 112/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0487 - accuracy: 0.9881 - val_loss: 2.3195 - val_accuracy: 0.7260
Epoch 113/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0442 - accuracy: 0.9905 - val_loss: 2.7354 - val_accuracy: 0.7064
Epoch 114/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0760 - accuracy: 0.9810 - val_loss: 3.0673 - val_accuracy: 0.7064
Epoch 115/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0597 - accuracy: 0.9870 - val_loss: 2.7083 - val_accuracy: 0.7046
Epoch 116/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0492 - accuracy: 0.9870 - val_loss: 2.6727 - val_accuracy: 0.7260
Epoch 117/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0749 - accuracy: 0.9798 - val_loss: 3.1423 - val_accuracy: 0.6957
Epoch 118/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0751 - accuracy: 0.9798 - val_loss: 3.0833 - val_accuracy: 0.6815
Epoch 119/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0483 - accuracy: 0.9858 - val_loss: 2.7395 - val_accuracy: 0.7384
Epoch 120/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1059 - accuracy: 0.9715 - val_loss: 2.5292 - val_accuracy: 0.7224
Epoch 121/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0813 - accuracy: 0.9775 - val_loss: 2.8451 - val_accuracy: 0.7028
Epoch 122/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1350 - accuracy: 0.9715 - val_loss: 2.3971 - val_accuracy: 0.7028
Epoch 123/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0775 - accuracy: 0.9751 - val_loss: 2.7584 - val_accuracy: 0.6815
Epoch 124/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1123 - accuracy: 0.9703 - val_loss: 2.4340 - val_accuracy: 0.7242
Epoch 125/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1581 - accuracy: 0.9597 - val_loss: 2.5691 - val_accuracy: 0.7011
Epoch 126/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1133 - accuracy: 0.9668 - val_loss: 2.7336 - val_accuracy: 0.7100
Epoch 127/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0739 - accuracy: 0.9775 - val_loss: 2.4535 - val_accuracy: 0.7082
Epoch 128/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0466 - accuracy: 0.9881 - val_loss: 2.8451 - val_accuracy: 0.7171
Epoch 129/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0487 - accuracy: 0.9858 - val_loss: 2.3166 - val_accuracy: 0.7206
Epoch 130/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0975 - accuracy: 0.9751 - val_loss: 2.8314 - val_accuracy: 0.7028
Epoch 131/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9786 - val_loss: 2.6808 - val_accuracy: 0.7153
Epoch 132/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1320 - accuracy: 0.9692 - val_loss: 2.2271 - val_accuracy: 0.7189
Epoch 133/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0586 - accuracy: 0.9786 - val_loss: 2.0880 - val_accuracy: 0.7189
Epoch 134/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1306 - accuracy: 0.9739 - val_loss: 2.1496 - val_accuracy: 0.7171
Epoch 135/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1912 - accuracy: 0.9597 - val_loss: 2.0640 - val_accuracy: 0.7278
Epoch 136/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0789 - accuracy: 0.9751 - val_loss: 1.8647 - val_accuracy: 0.7384
Epoch 137/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0437 - accuracy: 0.9858 - val_loss: 2.1268 - val_accuracy: 0.7242
Epoch 138/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0680 - accuracy: 0.9822 - val_loss: 2.5175 - val_accuracy: 0.7135
Epoch 139/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0564 - accuracy: 0.9858 - val_loss: 2.1744 - val_accuracy: 0.7420
Epoch 140/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0759 - accuracy: 0.9846 - val_loss: 2.2400 - val_accuracy: 0.7189
Epoch 141/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0349 - accuracy: 0.9905 - val_loss: 2.8518 - val_accuracy: 0.7064
Epoch 142/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0437 - accuracy: 0.9870 - val_loss: 3.0157 - val_accuracy: 0.7064
Epoch 143/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0607 - accuracy: 0.9858 - val_loss: 2.7773 - val_accuracy: 0.7224
Epoch 144/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0401 - accuracy: 0.9905 - val_loss: 3.1259 - val_accuracy: 0.7011
Epoch 145/750
27/27 [==============================] - 0s 3ms/step - loss: 0.1470 - accuracy: 0.9609 - val_loss: 2.3876 - val_accuracy: 0.7082
Epoch 146/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0803 - accuracy: 0.9763 - val_loss: 2.7426 - val_accuracy: 0.6833
Epoch 147/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0358 - accuracy: 0.9929 - val_loss: 2.4578 - val_accuracy: 0.7331
Epoch 148/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0335 - accuracy: 0.9905 - val_loss: 2.7366 - val_accuracy: 0.6993
Epoch 149/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0630 - accuracy: 0.9822 - val_loss: 2.6638 - val_accuracy: 0.7135
Epoch 150/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3283 - accuracy: 0.9146 - val_loss: 1.8602 - val_accuracy: 0.7278
Epoch 151/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1388 - accuracy: 0.9573 - val_loss: 1.9389 - val_accuracy: 0.7028
Epoch 152/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0572 - accuracy: 0.9893 - val_loss: 2.5810 - val_accuracy: 0.7028
Epoch 153/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0338 - accuracy: 0.9881 - val_loss: 2.5342 - val_accuracy: 0.7100
Epoch 154/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0251 - accuracy: 0.9953 - val_loss: 2.4494 - val_accuracy: 0.7295
Epoch 155/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0410 - accuracy: 0.9929 - val_loss: 2.5263 - val_accuracy: 0.7224
Epoch 156/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0232 - accuracy: 0.9929 - val_loss: 2.7234 - val_accuracy: 0.7206
Epoch 157/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0226 - accuracy: 0.9917 - val_loss: 2.7856 - val_accuracy: 0.7224
Epoch 158/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0108 - accuracy: 0.9964 - val_loss: 2.9120 - val_accuracy: 0.7278
Epoch 159/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0290 - accuracy: 0.9964 - val_loss: 2.8644 - val_accuracy: 0.7153
Epoch 160/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0101 - accuracy: 0.9988 - val_loss: 3.0894 - val_accuracy: 0.7171
Epoch 161/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0390 - accuracy: 0.9953 - val_loss: 2.7201 - val_accuracy: 0.7171
Epoch 162/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0074 - accuracy: 0.9988 - val_loss: 2.8819 - val_accuracy: 0.7135
Epoch 163/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 2.9555 - val_accuracy: 0.7100
Epoch 164/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 2.9939 - val_accuracy: 0.7100
Epoch 165/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0090 - accuracy: 0.9976 - val_loss: 3.2253 - val_accuracy: 0.7135
Epoch 166/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 3.1989 - val_accuracy: 0.7171
Epoch 167/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 3.2011 - val_accuracy: 0.7278
Epoch 168/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 3.2859 - val_accuracy: 0.7189
Epoch 169/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9988 - val_loss: 3.0473 - val_accuracy: 0.7064
Epoch 170/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1165 - accuracy: 0.9763 - val_loss: 3.4154 - val_accuracy: 0.6762
Epoch 171/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2350 - accuracy: 0.9431 - val_loss: 2.6813 - val_accuracy: 0.6673
Epoch 172/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1695 - accuracy: 0.9585 - val_loss: 1.9494 - val_accuracy: 0.7473
Epoch 173/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0763 - accuracy: 0.9810 - val_loss: 2.1570 - val_accuracy: 0.7260
Epoch 174/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0386 - accuracy: 0.9953 - val_loss: 2.3837 - val_accuracy: 0.7295
Epoch 175/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0557 - accuracy: 0.9870 - val_loss: 2.3838 - val_accuracy: 0.7100
Epoch 176/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1462 - accuracy: 0.9632 - val_loss: 2.2428 - val_accuracy: 0.7153
Epoch 177/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0475 - accuracy: 0.9881 - val_loss: 2.2247 - val_accuracy: 0.7331
Epoch 178/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0421 - accuracy: 0.9929 - val_loss: 2.6587 - val_accuracy: 0.7064
Epoch 179/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0207 - accuracy: 0.9929 - val_loss: 2.5285 - val_accuracy: 0.7367
Epoch 180/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0338 - accuracy: 0.9905 - val_loss: 2.6069 - val_accuracy: 0.7206
Epoch 181/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0489 - accuracy: 0.9858 - val_loss: 2.5485 - val_accuracy: 0.7242
Epoch 182/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0949 - accuracy: 0.9763 - val_loss: 2.6366 - val_accuracy: 0.7242
Epoch 183/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0983 - accuracy: 0.9846 - val_loss: 2.2125 - val_accuracy: 0.7260
Epoch 184/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0481 - accuracy: 0.9870 - val_loss: 2.6907 - val_accuracy: 0.7064
Epoch 185/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0689 - accuracy: 0.9870 - val_loss: 2.7806 - val_accuracy: 0.6779
Epoch 186/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1118 - accuracy: 0.9703 - val_loss: 2.4077 - val_accuracy: 0.7224
Epoch 187/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0915 - accuracy: 0.9775 - val_loss: 2.1227 - val_accuracy: 0.7206
Epoch 188/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0432 - accuracy: 0.9870 - val_loss: 2.1678 - val_accuracy: 0.7224
Epoch 189/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0260 - accuracy: 0.9917 - val_loss: 2.2623 - val_accuracy: 0.7349
Epoch 190/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0166 - accuracy: 0.9964 - val_loss: 2.7294 - val_accuracy: 0.7278
Epoch 191/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0478 - accuracy: 0.9846 - val_loss: 2.6692 - val_accuracy: 0.7313
Epoch 192/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1453 - accuracy: 0.9727 - val_loss: 3.1524 - val_accuracy: 0.6548
Epoch 193/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0722 - accuracy: 0.9870 - val_loss: 2.4376 - val_accuracy: 0.7046
Epoch 194/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0194 - accuracy: 0.9988 - val_loss: 2.5307 - val_accuracy: 0.7189
Epoch 195/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0146 - accuracy: 0.9976 - val_loss: 2.8084 - val_accuracy: 0.7117
Epoch 196/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0112 - accuracy: 0.9964 - val_loss: 2.9439 - val_accuracy: 0.7367
Epoch 197/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0101 - accuracy: 0.9964 - val_loss: 2.6865 - val_accuracy: 0.7331
Epoch 198/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 2.6923 - val_accuracy: 0.7367
Epoch 199/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0078 - accuracy: 0.9988 - val_loss: 2.7639 - val_accuracy: 0.7295
Epoch 200/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0161 - accuracy: 0.9964 - val_loss: 2.8118 - val_accuracy: 0.7313
Epoch 201/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 2.9268 - val_accuracy: 0.7313
Epoch 202/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 2.9835 - val_accuracy: 0.7260
Epoch 203/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0095 - accuracy: 0.9988 - val_loss: 2.9774 - val_accuracy: 0.7331
Epoch 204/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 3.0556 - val_accuracy: 0.7331
Epoch 205/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 3.1025 - val_accuracy: 0.7260
Epoch 206/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 3.1800 - val_accuracy: 0.7260
Epoch 207/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0058 - accuracy: 0.9976 - val_loss: 3.3378 - val_accuracy: 0.7189
Epoch 208/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 3.2910 - val_accuracy: 0.7224
Epoch 209/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0150 - accuracy: 0.9964 - val_loss: 3.3976 - val_accuracy: 0.7224
Epoch 210/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2012 - accuracy: 0.9609 - val_loss: 3.0926 - val_accuracy: 0.6601
Epoch 211/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1765 - accuracy: 0.9526 - val_loss: 2.0000 - val_accuracy: 0.6993
Epoch 212/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1373 - accuracy: 0.9620 - val_loss: 2.1702 - val_accuracy: 0.7028
Epoch 213/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0453 - accuracy: 0.9929 - val_loss: 2.1698 - val_accuracy: 0.7367
Epoch 214/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0121 - accuracy: 0.9988 - val_loss: 2.4070 - val_accuracy: 0.7402
Epoch 215/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 2.5191 - val_accuracy: 0.7402
Epoch 216/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 2.6325 - val_accuracy: 0.7367
Epoch 217/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 2.7147 - val_accuracy: 0.7402
Epoch 218/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 2.7756 - val_accuracy: 0.7438
Epoch 219/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 2.6979 - val_accuracy: 0.7473
Epoch 220/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0115 - accuracy: 0.9988 - val_loss: 2.7965 - val_accuracy: 0.7402
Epoch 221/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 2.8646 - val_accuracy: 0.7420
Epoch 222/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.9624 - val_accuracy: 0.7420
Epoch 223/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 3.0109 - val_accuracy: 0.7473
Epoch 224/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 3.0605 - val_accuracy: 0.7438
Epoch 225/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.0902 - val_accuracy: 0.7456
Epoch 226/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 3.1155 - val_accuracy: 0.7420
Epoch 227/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 3.1561 - val_accuracy: 0.7331
Epoch 228/750
27/27 [==============================] - 0s 2ms/step - loss: 6.1160e-04 - accuracy: 1.0000 - val_loss: 3.1875 - val_accuracy: 0.7278
Epoch 229/750
27/27 [==============================] - 0s 2ms/step - loss: 9.0775e-04 - accuracy: 1.0000 - val_loss: 3.2238 - val_accuracy: 0.7295
Epoch 230/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 3.2862 - val_accuracy: 0.7313
Epoch 231/750
27/27 [==============================] - 0s 2ms/step - loss: 5.4172e-04 - accuracy: 1.0000 - val_loss: 3.3041 - val_accuracy: 0.7367
Epoch 232/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.3559 - val_accuracy: 0.7331
Epoch 233/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0124 - accuracy: 0.9988 - val_loss: 3.4165 - val_accuracy: 0.7313
Epoch 234/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0099 - accuracy: 0.9976 - val_loss: 3.5069 - val_accuracy: 0.7242
Epoch 235/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0109 - accuracy: 0.9953 - val_loss: 3.5610 - val_accuracy: 0.7224
Epoch 236/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0936 - accuracy: 0.9798 - val_loss: 3.2404 - val_accuracy: 0.7100
Epoch 237/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3034 - accuracy: 0.9336 - val_loss: 1.8618 - val_accuracy: 0.7100
Epoch 238/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2282 - accuracy: 0.9265 - val_loss: 2.1351 - val_accuracy: 0.7384
Epoch 239/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0931 - accuracy: 0.9751 - val_loss: 2.1132 - val_accuracy: 0.7331
Epoch 240/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1588 - accuracy: 0.9620 - val_loss: 2.2224 - val_accuracy: 0.7064
Epoch 241/750
27/27 [==============================] - 0s 3ms/step - loss: 0.1958 - accuracy: 0.9490 - val_loss: 2.1821 - val_accuracy: 0.7206
Epoch 242/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0639 - accuracy: 0.9822 - val_loss: 2.3030 - val_accuracy: 0.7278
Epoch 243/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0190 - accuracy: 0.9953 - val_loss: 2.6708 - val_accuracy: 0.7189
Epoch 244/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0699 - accuracy: 0.9846 - val_loss: 2.4602 - val_accuracy: 0.7224
Epoch 245/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0302 - accuracy: 0.9905 - val_loss: 2.4979 - val_accuracy: 0.7384
Epoch 246/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0325 - accuracy: 0.9905 - val_loss: 2.7825 - val_accuracy: 0.7295
Epoch 247/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2858 - accuracy: 0.9537 - val_loss: 1.6419 - val_accuracy: 0.6940
Epoch 248/750
27/27 [==============================] - 0s 3ms/step - loss: 0.1959 - accuracy: 0.9502 - val_loss: 1.8806 - val_accuracy: 0.7011
Epoch 249/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0531 - accuracy: 0.9786 - val_loss: 2.1255 - val_accuracy: 0.7189
Epoch 250/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0265 - accuracy: 0.9893 - val_loss: 2.1904 - val_accuracy: 0.7313
Epoch 251/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0135 - accuracy: 0.9964 - val_loss: 2.2953 - val_accuracy: 0.7313
Epoch 252/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 2.3132 - val_accuracy: 0.7367
Epoch 253/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 2.3438 - val_accuracy: 0.7402
Epoch 254/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 2.4511 - val_accuracy: 0.7349
Epoch 255/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.5183 - val_accuracy: 0.7295
Epoch 256/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 2.5362 - val_accuracy: 0.7420
Epoch 257/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 2.6393 - val_accuracy: 0.7349
Epoch 258/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.7032 - val_accuracy: 0.7402
Epoch 259/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.7364 - val_accuracy: 0.7367
Epoch 260/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 2.7214 - val_accuracy: 0.7367
Epoch 261/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 2.8116 - val_accuracy: 0.7402
Epoch 262/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8467 - val_accuracy: 0.7402
Epoch 263/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 0.9988 - val_loss: 3.0192 - val_accuracy: 0.7367
Epoch 264/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.9273 - val_accuracy: 0.7384
Epoch 265/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.9375 - val_accuracy: 0.7384
Epoch 266/750
27/27 [==============================] - 0s 3ms/step - loss: 6.2781e-04 - accuracy: 1.0000 - val_loss: 3.0661 - val_accuracy: 0.7331
Epoch 267/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 3.1646 - val_accuracy: 0.7278
Epoch 268/750
27/27 [==============================] - 0s 3ms/step - loss: 6.3274e-04 - accuracy: 1.0000 - val_loss: 3.1936 - val_accuracy: 0.7278
Epoch 269/750
27/27 [==============================] - 0s 3ms/step - loss: 7.6470e-04 - accuracy: 1.0000 - val_loss: 3.2320 - val_accuracy: 0.7278
Epoch 270/750
27/27 [==============================] - 0s 3ms/step - loss: 4.4165e-04 - accuracy: 1.0000 - val_loss: 3.2517 - val_accuracy: 0.7260
Epoch 271/750
27/27 [==============================] - 0s 3ms/step - loss: 6.6438e-04 - accuracy: 1.0000 - val_loss: 3.3285 - val_accuracy: 0.7295
Epoch 272/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 0.9988 - val_loss: 3.3192 - val_accuracy: 0.7278
Epoch 273/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 3.3485 - val_accuracy: 0.7295
Epoch 274/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0075 - accuracy: 0.9988 - val_loss: 3.3331 - val_accuracy: 0.7456
Epoch 275/750
27/27 [==============================] - 0s 3ms/step - loss: 0.2357 - accuracy: 0.9609 - val_loss: 2.0632 - val_accuracy: 0.7260
Epoch 276/750
27/27 [==============================] - 0s 3ms/step - loss: 0.1214 - accuracy: 0.9656 - val_loss: 2.1854 - val_accuracy: 0.7260
Epoch 277/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0763 - accuracy: 0.9846 - val_loss: 2.0521 - val_accuracy: 0.7189
Epoch 278/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2339 - accuracy: 0.9537 - val_loss: 2.4165 - val_accuracy: 0.6815
Epoch 279/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2282 - accuracy: 0.9514 - val_loss: 1.8125 - val_accuracy: 0.7011
Epoch 280/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1024 - accuracy: 0.9798 - val_loss: 1.6958 - val_accuracy: 0.7295
Epoch 281/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0230 - accuracy: 0.9976 - val_loss: 1.9848 - val_accuracy: 0.7402
Epoch 282/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0361 - accuracy: 0.9929 - val_loss: 2.3473 - val_accuracy: 0.7046
Epoch 283/750
27/27 [==============================] - 0s 3ms/step - loss: 0.1309 - accuracy: 0.9715 - val_loss: 2.1190 - val_accuracy: 0.7117
Epoch 284/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0704 - accuracy: 0.9858 - val_loss: 1.8809 - val_accuracy: 0.7260
Epoch 285/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0536 - accuracy: 0.9822 - val_loss: 2.4380 - val_accuracy: 0.7189
Epoch 286/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0221 - accuracy: 0.9953 - val_loss: 2.5926 - val_accuracy: 0.7117
Epoch 287/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0132 - accuracy: 0.9953 - val_loss: 2.5015 - val_accuracy: 0.7242
Epoch 288/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0073 - accuracy: 0.9988 - val_loss: 2.5928 - val_accuracy: 0.7295
Epoch 289/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 2.7265 - val_accuracy: 0.7260
Epoch 290/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.7759 - val_accuracy: 0.7224
Epoch 291/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.8071 - val_accuracy: 0.7224
Epoch 292/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8511 - val_accuracy: 0.7278
Epoch 293/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0117 - accuracy: 0.9976 - val_loss: 2.9226 - val_accuracy: 0.7278
Epoch 294/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 2.8455 - val_accuracy: 0.7349
Epoch 295/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 2.8935 - val_accuracy: 0.7367
Epoch 296/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 3.0042 - val_accuracy: 0.7278
Epoch 297/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 3.0294 - val_accuracy: 0.7349
Epoch 298/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 0.9988 - val_loss: 2.9778 - val_accuracy: 0.7402
Epoch 299/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 3.0298 - val_accuracy: 0.7384
Epoch 300/750
27/27 [==============================] - 0s 3ms/step - loss: 6.4037e-04 - accuracy: 1.0000 - val_loss: 3.0697 - val_accuracy: 0.7384
Epoch 301/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.1421 - val_accuracy: 0.7367
Epoch 302/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0022 - accuracy: 0.9988 - val_loss: 3.1451 - val_accuracy: 0.7331
Epoch 303/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0169 - accuracy: 0.9976 - val_loss: 3.1826 - val_accuracy: 0.7420
Epoch 304/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 3.3966 - val_accuracy: 0.7242
Epoch 305/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0178 - accuracy: 0.9953 - val_loss: 3.2664 - val_accuracy: 0.7189
Epoch 306/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 0.9964 - val_loss: 3.8920 - val_accuracy: 0.6993
Epoch 307/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0451 - accuracy: 0.9858 - val_loss: 3.6572 - val_accuracy: 0.7011
Epoch 308/750
27/27 [==============================] - 0s 2ms/step - loss: 0.3524 - accuracy: 0.9359 - val_loss: 1.9492 - val_accuracy: 0.7028
Epoch 309/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1948 - accuracy: 0.9561 - val_loss: 1.5438 - val_accuracy: 0.7224
Epoch 310/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1089 - accuracy: 0.9727 - val_loss: 1.7426 - val_accuracy: 0.7117
Epoch 311/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0300 - accuracy: 0.9929 - val_loss: 2.1965 - val_accuracy: 0.7064
Epoch 312/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0184 - accuracy: 0.9953 - val_loss: 2.4218 - val_accuracy: 0.7242
Epoch 313/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0939 - accuracy: 0.9822 - val_loss: 2.3083 - val_accuracy: 0.7082
Epoch 314/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 2.2961 - val_accuracy: 0.7064
Epoch 315/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0160 - accuracy: 0.9976 - val_loss: 2.0570 - val_accuracy: 0.7117
Epoch 316/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0290 - accuracy: 0.9929 - val_loss: 2.4621 - val_accuracy: 0.7189
Epoch 317/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0453 - accuracy: 0.9893 - val_loss: 2.2261 - val_accuracy: 0.7171
Epoch 318/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0834 - accuracy: 0.9917 - val_loss: 2.3347 - val_accuracy: 0.7313
Epoch 319/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0265 - accuracy: 0.9929 - val_loss: 2.2477 - val_accuracy: 0.7367
Epoch 320/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0095 - accuracy: 0.9988 - val_loss: 2.2652 - val_accuracy: 0.7331
Epoch 321/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0179 - accuracy: 0.9953 - val_loss: 2.2946 - val_accuracy: 0.7313
Epoch 322/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0351 - accuracy: 0.9964 - val_loss: 2.3923 - val_accuracy: 0.7028
Epoch 323/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0718 - accuracy: 0.9858 - val_loss: 2.0942 - val_accuracy: 0.7153
Epoch 324/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0439 - accuracy: 0.9893 - val_loss: 2.1651 - val_accuracy: 0.7331
Epoch 325/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0233 - accuracy: 0.9988 - val_loss: 2.0932 - val_accuracy: 0.7278
Epoch 326/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 2.0903 - val_accuracy: 0.7349
Epoch 327/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 2.2155 - val_accuracy: 0.7278
Epoch 328/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0288 - accuracy: 0.9941 - val_loss: 2.1931 - val_accuracy: 0.7242
Epoch 329/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0108 - accuracy: 0.9988 - val_loss: 2.2513 - val_accuracy: 0.7402
Epoch 330/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0527 - accuracy: 0.9917 - val_loss: 2.6145 - val_accuracy: 0.7242
Epoch 331/750
27/27 [==============================] - 0s 3ms/step - loss: 0.1983 - accuracy: 0.9514 - val_loss: 2.2268 - val_accuracy: 0.6744
Epoch 332/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0934 - accuracy: 0.9763 - val_loss: 1.8926 - val_accuracy: 0.7242
Epoch 333/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0493 - accuracy: 0.9893 - val_loss: 1.9345 - val_accuracy: 0.7278
Epoch 334/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9941 - val_loss: 2.1532 - val_accuracy: 0.7171
Epoch 335/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0064 - accuracy: 0.9988 - val_loss: 2.3445 - val_accuracy: 0.7295
Epoch 336/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.3886 - val_accuracy: 0.7295
Epoch 337/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0059 - accuracy: 0.9988 - val_loss: 2.4480 - val_accuracy: 0.7295
Epoch 338/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 2.5372 - val_accuracy: 0.7313
Epoch 339/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 2.5822 - val_accuracy: 0.7295
Epoch 340/750
27/27 [==============================] - 0s 2ms/step - loss: 9.2874e-04 - accuracy: 1.0000 - val_loss: 2.6177 - val_accuracy: 0.7295
Epoch 341/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.6524 - val_accuracy: 0.7313
Epoch 342/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 2.7518 - val_accuracy: 0.7064
Epoch 343/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0102 - accuracy: 0.9976 - val_loss: 2.9018 - val_accuracy: 0.7367
Epoch 344/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1449 - accuracy: 0.9775 - val_loss: 2.1636 - val_accuracy: 0.7153
Epoch 345/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1822 - accuracy: 0.9442 - val_loss: 1.9320 - val_accuracy: 0.7260
Epoch 346/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0458 - accuracy: 0.9905 - val_loss: 1.9608 - val_accuracy: 0.7224
Epoch 347/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0291 - accuracy: 0.9929 - val_loss: 2.3290 - val_accuracy: 0.7135
Epoch 348/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0138 - accuracy: 0.9976 - val_loss: 2.3038 - val_accuracy: 0.7278
Epoch 349/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 2.4651 - val_accuracy: 0.7171
Epoch 350/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 2.4884 - val_accuracy: 0.7260
Epoch 351/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 2.5193 - val_accuracy: 0.7153
Epoch 352/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 2.4611 - val_accuracy: 0.7260
Epoch 353/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.4986 - val_accuracy: 0.7313
Epoch 354/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 2.5680 - val_accuracy: 0.7349
Epoch 355/750
27/27 [==============================] - 0s 2ms/step - loss: 8.0919e-04 - accuracy: 1.0000 - val_loss: 2.6006 - val_accuracy: 0.7331
Epoch 356/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.6304 - val_accuracy: 0.7331
Epoch 357/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0644 - accuracy: 0.9881 - val_loss: 3.2845 - val_accuracy: 0.6904
Epoch 358/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2044 - accuracy: 0.9502 - val_loss: 1.8251 - val_accuracy: 0.7082
Epoch 359/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0935 - accuracy: 0.9798 - val_loss: 1.7941 - val_accuracy: 0.7491
Epoch 360/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0958 - accuracy: 0.9763 - val_loss: 2.4638 - val_accuracy: 0.7206
Epoch 361/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1049 - accuracy: 0.9739 - val_loss: 1.7025 - val_accuracy: 0.7313
Epoch 362/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0350 - accuracy: 0.9941 - val_loss: 2.1756 - val_accuracy: 0.7295
Epoch 363/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0647 - accuracy: 0.9870 - val_loss: 1.9901 - val_accuracy: 0.7153
Epoch 364/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0178 - accuracy: 0.9976 - val_loss: 2.1253 - val_accuracy: 0.7420
Epoch 365/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0097 - accuracy: 0.9988 - val_loss: 2.1633 - val_accuracy: 0.7260
Epoch 366/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 2.2702 - val_accuracy: 0.7438
Epoch 367/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 2.3735 - val_accuracy: 0.7473
Epoch 368/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 0.9988 - val_loss: 2.4477 - val_accuracy: 0.7420
Epoch 369/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.4936 - val_accuracy: 0.7367
Epoch 370/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 2.5592 - val_accuracy: 0.7313
Epoch 371/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.6183 - val_accuracy: 0.7313
Epoch 372/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.6629 - val_accuracy: 0.7313
Epoch 373/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.6935 - val_accuracy: 0.7295
Epoch 374/750
27/27 [==============================] - 0s 2ms/step - loss: 8.1192e-04 - accuracy: 1.0000 - val_loss: 2.7226 - val_accuracy: 0.7295
Epoch 375/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.7517 - val_accuracy: 0.7313
Epoch 376/750
27/27 [==============================] - 0s 2ms/step - loss: 8.3450e-04 - accuracy: 1.0000 - val_loss: 2.7732 - val_accuracy: 0.7331
Epoch 377/750
27/27 [==============================] - 0s 2ms/step - loss: 4.7015e-04 - accuracy: 1.0000 - val_loss: 2.8098 - val_accuracy: 0.7313
Epoch 378/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 2.8417 - val_accuracy: 0.7278
Epoch 379/750
27/27 [==============================] - 0s 2ms/step - loss: 4.0949e-04 - accuracy: 1.0000 - val_loss: 2.8228 - val_accuracy: 0.7278
Epoch 380/750
27/27 [==============================] - 0s 3ms/step - loss: 5.2127e-04 - accuracy: 1.0000 - val_loss: 2.8552 - val_accuracy: 0.7278
Epoch 381/750
27/27 [==============================] - 0s 2ms/step - loss: 8.1901e-04 - accuracy: 1.0000 - val_loss: 2.8819 - val_accuracy: 0.7278
Epoch 382/750
27/27 [==============================] - 0s 2ms/step - loss: 6.1702e-04 - accuracy: 1.0000 - val_loss: 2.9025 - val_accuracy: 0.7313
Epoch 383/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.9432 - val_accuracy: 0.7313
Epoch 384/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0035 - accuracy: 0.9988 - val_loss: 3.0012 - val_accuracy: 0.7313
Epoch 385/750
27/27 [==============================] - 0s 2ms/step - loss: 6.6587e-04 - accuracy: 1.0000 - val_loss: 3.0291 - val_accuracy: 0.7384
Epoch 386/750
27/27 [==============================] - 0s 2ms/step - loss: 1.9959e-04 - accuracy: 1.0000 - val_loss: 3.0482 - val_accuracy: 0.7402
Epoch 387/750
27/27 [==============================] - 0s 2ms/step - loss: 1.9021e-04 - accuracy: 1.0000 - val_loss: 3.0567 - val_accuracy: 0.7402
Epoch 388/750
27/27 [==============================] - 0s 2ms/step - loss: 4.5242e-04 - accuracy: 1.0000 - val_loss: 3.0820 - val_accuracy: 0.7402
Epoch 389/750
27/27 [==============================] - 0s 2ms/step - loss: 3.1029e-04 - accuracy: 1.0000 - val_loss: 3.0935 - val_accuracy: 0.7384
Epoch 390/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 3.1241 - val_accuracy: 0.7367
Epoch 391/750
27/27 [==============================] - 0s 2ms/step - loss: 5.0755e-04 - accuracy: 1.0000 - val_loss: 3.1338 - val_accuracy: 0.7384
Epoch 392/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 3.1766 - val_accuracy: 0.7349
Epoch 393/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9988 - val_loss: 3.1709 - val_accuracy: 0.7402
Epoch 394/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.1893 - val_accuracy: 0.7384
Epoch 395/750
27/27 [==============================] - 0s 2ms/step - loss: 1.7208e-04 - accuracy: 1.0000 - val_loss: 3.2080 - val_accuracy: 0.7367
Epoch 396/750
27/27 [==============================] - 0s 2ms/step - loss: 4.3117e-04 - accuracy: 1.0000 - val_loss: 3.1995 - val_accuracy: 0.7384
Epoch 397/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 3.2498 - val_accuracy: 0.7331
Epoch 398/750
27/27 [==============================] - 0s 2ms/step - loss: 2.3678e-04 - accuracy: 1.0000 - val_loss: 3.2531 - val_accuracy: 0.7349
Epoch 399/750
27/27 [==============================] - 0s 2ms/step - loss: 8.8592e-04 - accuracy: 1.0000 - val_loss: 3.3422 - val_accuracy: 0.7260
Epoch 400/750
27/27 [==============================] - 0s 2ms/step - loss: 9.3238e-05 - accuracy: 1.0000 - val_loss: 3.3707 - val_accuracy: 0.7295
Epoch 401/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 3.3808 - val_accuracy: 0.7295
Epoch 402/750
27/27 [==============================] - 0s 2ms/step - loss: 2.4233e-04 - accuracy: 1.0000 - val_loss: 3.3924 - val_accuracy: 0.7313
Epoch 403/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0055 - accuracy: 0.9964 - val_loss: 3.3275 - val_accuracy: 0.7278
Epoch 404/750
27/27 [==============================] - 0s 2ms/step - loss: 7.6027e-04 - accuracy: 1.0000 - val_loss: 3.3250 - val_accuracy: 0.7242
Epoch 405/750
27/27 [==============================] - 0s 2ms/step - loss: 1.1993e-04 - accuracy: 1.0000 - val_loss: 3.3155 - val_accuracy: 0.7278
Epoch 406/750
27/27 [==============================] - 0s 2ms/step - loss: 1.3929e-04 - accuracy: 1.0000 - val_loss: 3.3241 - val_accuracy: 0.7295
Epoch 407/750
27/27 [==============================] - 0s 2ms/step - loss: 3.5348e-04 - accuracy: 1.0000 - val_loss: 3.3580 - val_accuracy: 0.7295
Epoch 408/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 3.4026 - val_accuracy: 0.7260
Epoch 409/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1946 - accuracy: 0.9727 - val_loss: 2.7092 - val_accuracy: 0.6726
Epoch 410/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1217 - accuracy: 0.9668 - val_loss: 2.0425 - val_accuracy: 0.7295
Epoch 411/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0771 - accuracy: 0.9798 - val_loss: 2.3131 - val_accuracy: 0.6851
Epoch 412/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1367 - accuracy: 0.9668 - val_loss: 2.4093 - val_accuracy: 0.6851
Epoch 413/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0572 - accuracy: 0.9846 - val_loss: 2.0193 - val_accuracy: 0.7242
Epoch 414/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0405 - accuracy: 0.9893 - val_loss: 1.9492 - val_accuracy: 0.7295
Epoch 415/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0120 - accuracy: 0.9964 - val_loss: 2.1822 - val_accuracy: 0.7313
Epoch 416/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 2.4373 - val_accuracy: 0.7242
Epoch 417/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0510 - accuracy: 0.9893 - val_loss: 2.3273 - val_accuracy: 0.7135
Epoch 418/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0519 - accuracy: 0.9893 - val_loss: 2.1818 - val_accuracy: 0.7313
Epoch 419/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 2.1095 - val_accuracy: 0.7278
Epoch 420/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0090 - accuracy: 0.9976 - val_loss: 2.4219 - val_accuracy: 0.7313
Epoch 421/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0119 - accuracy: 0.9964 - val_loss: 2.3927 - val_accuracy: 0.7349
Epoch 422/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0084 - accuracy: 0.9988 - val_loss: 2.4085 - val_accuracy: 0.7367
Epoch 423/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.5279 - val_accuracy: 0.7313
Epoch 424/750
27/27 [==============================] - 0s 2ms/step - loss: 5.9919e-04 - accuracy: 1.0000 - val_loss: 2.5602 - val_accuracy: 0.7313
Epoch 425/750
27/27 [==============================] - 0s 2ms/step - loss: 8.8199e-04 - accuracy: 1.0000 - val_loss: 2.5571 - val_accuracy: 0.7384
Epoch 426/750
27/27 [==============================] - 0s 2ms/step - loss: 7.2639e-04 - accuracy: 1.0000 - val_loss: 2.5963 - val_accuracy: 0.7384
Epoch 427/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.7358 - val_accuracy: 0.7349
Epoch 428/750
27/27 [==============================] - 0s 2ms/step - loss: 5.4457e-04 - accuracy: 1.0000 - val_loss: 2.7915 - val_accuracy: 0.7367
Epoch 429/750
27/27 [==============================] - 0s 2ms/step - loss: 5.2305e-04 - accuracy: 1.0000 - val_loss: 2.8235 - val_accuracy: 0.7402
Epoch 430/750
27/27 [==============================] - 0s 2ms/step - loss: 4.8380e-04 - accuracy: 1.0000 - val_loss: 2.8465 - val_accuracy: 0.7420
Epoch 431/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.8575 - val_accuracy: 0.7384
Epoch 432/750
27/27 [==============================] - 0s 2ms/step - loss: 2.2673e-04 - accuracy: 1.0000 - val_loss: 2.8802 - val_accuracy: 0.7384
Epoch 433/750
27/27 [==============================] - 0s 2ms/step - loss: 4.9437e-04 - accuracy: 1.0000 - val_loss: 2.9089 - val_accuracy: 0.7402
Epoch 434/750
27/27 [==============================] - 0s 2ms/step - loss: 3.4534e-04 - accuracy: 1.0000 - val_loss: 2.9451 - val_accuracy: 0.7420
Epoch 435/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.9772 - val_accuracy: 0.7402
Epoch 436/750
27/27 [==============================] - 0s 2ms/step - loss: 5.1711e-04 - accuracy: 1.0000 - val_loss: 2.9826 - val_accuracy: 0.7438
Epoch 437/750
27/27 [==============================] - 0s 2ms/step - loss: 3.4997e-04 - accuracy: 1.0000 - val_loss: 3.0134 - val_accuracy: 0.7438
Epoch 438/750
27/27 [==============================] - 0s 2ms/step - loss: 7.7957e-04 - accuracy: 1.0000 - val_loss: 3.1107 - val_accuracy: 0.7473
Epoch 439/750
27/27 [==============================] - 0s 2ms/step - loss: 2.1987e-04 - accuracy: 1.0000 - val_loss: 3.1170 - val_accuracy: 0.7438
Epoch 440/750
27/27 [==============================] - 0s 2ms/step - loss: 1.9475e-04 - accuracy: 1.0000 - val_loss: 3.1192 - val_accuracy: 0.7438
Epoch 441/750
27/27 [==============================] - 0s 2ms/step - loss: 6.8207e-04 - accuracy: 1.0000 - val_loss: 3.1791 - val_accuracy: 0.7456
Epoch 442/750
27/27 [==============================] - 0s 2ms/step - loss: 4.5976e-04 - accuracy: 1.0000 - val_loss: 3.2098 - val_accuracy: 0.7473
Epoch 443/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2802e-04 - accuracy: 1.0000 - val_loss: 3.2216 - val_accuracy: 0.7456
Epoch 444/750
27/27 [==============================] - 0s 2ms/step - loss: 5.0311e-04 - accuracy: 1.0000 - val_loss: 3.1958 - val_accuracy: 0.7402
Epoch 445/750
27/27 [==============================] - 0s 2ms/step - loss: 1.7676e-04 - accuracy: 1.0000 - val_loss: 3.2110 - val_accuracy: 0.7402
Epoch 446/750
27/27 [==============================] - 0s 2ms/step - loss: 9.6274e-05 - accuracy: 1.0000 - val_loss: 3.2267 - val_accuracy: 0.7402
Epoch 447/750
27/27 [==============================] - 0s 2ms/step - loss: 3.6923e-04 - accuracy: 1.0000 - val_loss: 3.2445 - val_accuracy: 0.7456
Epoch 448/750
27/27 [==============================] - 0s 2ms/step - loss: 2.3819e-04 - accuracy: 1.0000 - val_loss: 3.2771 - val_accuracy: 0.7491
Epoch 449/750
27/27 [==============================] - 0s 2ms/step - loss: 5.4908e-04 - accuracy: 1.0000 - val_loss: 3.2572 - val_accuracy: 0.7491
Epoch 450/750
27/27 [==============================] - 0s 2ms/step - loss: 1.4316e-04 - accuracy: 1.0000 - val_loss: 3.2139 - val_accuracy: 0.7491
Epoch 451/750
27/27 [==============================] - 0s 2ms/step - loss: 2.3990e-04 - accuracy: 1.0000 - val_loss: 3.2421 - val_accuracy: 0.7509
Epoch 452/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0703e-04 - accuracy: 1.0000 - val_loss: 3.2883 - val_accuracy: 0.7509
Epoch 453/750
27/27 [==============================] - 0s 2ms/step - loss: 9.8184e-04 - accuracy: 1.0000 - val_loss: 3.2861 - val_accuracy: 0.7509
Epoch 454/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0253e-04 - accuracy: 1.0000 - val_loss: 3.2935 - val_accuracy: 0.7509
Epoch 455/750
27/27 [==============================] - 0s 2ms/step - loss: 3.7305e-04 - accuracy: 1.0000 - val_loss: 3.3186 - val_accuracy: 0.7473
Epoch 456/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 3.5002 - val_accuracy: 0.7473
Epoch 457/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1906 - accuracy: 0.9692 - val_loss: 2.1768 - val_accuracy: 0.7260
Epoch 458/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2007 - accuracy: 0.9537 - val_loss: 2.0831 - val_accuracy: 0.7206
Epoch 459/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1256 - accuracy: 0.9632 - val_loss: 1.9294 - val_accuracy: 0.6993
Epoch 460/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0881 - accuracy: 0.9775 - val_loss: 2.4835 - val_accuracy: 0.6922
Epoch 461/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0671 - accuracy: 0.9822 - val_loss: 1.8677 - val_accuracy: 0.7456
Epoch 462/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0522 - accuracy: 0.9893 - val_loss: 2.4362 - val_accuracy: 0.7135
Epoch 463/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0588 - accuracy: 0.9846 - val_loss: 3.2568 - val_accuracy: 0.7011
Epoch 464/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1181 - accuracy: 0.9715 - val_loss: 2.1625 - val_accuracy: 0.7224
Epoch 465/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0899 - accuracy: 0.9810 - val_loss: 2.2972 - val_accuracy: 0.7153
Epoch 466/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0996 - accuracy: 0.9775 - val_loss: 1.9900 - val_accuracy: 0.7278
Epoch 467/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1817 - accuracy: 0.9597 - val_loss: 1.5655 - val_accuracy: 0.7260
Epoch 468/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0649 - accuracy: 0.9905 - val_loss: 1.9271 - val_accuracy: 0.7224
Epoch 469/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9953 - val_loss: 2.2055 - val_accuracy: 0.7100
Epoch 470/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0093 - accuracy: 0.9988 - val_loss: 2.3275 - val_accuracy: 0.7260
Epoch 471/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 2.4754 - val_accuracy: 0.7313
Epoch 472/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 2.5562 - val_accuracy: 0.7189
Epoch 473/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.6200 - val_accuracy: 0.7206
Epoch 474/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.6647 - val_accuracy: 0.7242
Epoch 475/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 2.6885 - val_accuracy: 0.7260
Epoch 476/750
27/27 [==============================] - 0s 2ms/step - loss: 9.5887e-04 - accuracy: 1.0000 - val_loss: 2.7452 - val_accuracy: 0.7242
Epoch 477/750
27/27 [==============================] - 0s 2ms/step - loss: 5.6764e-04 - accuracy: 1.0000 - val_loss: 2.7647 - val_accuracy: 0.7278
Epoch 478/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8167 - val_accuracy: 0.7278
Epoch 479/750
27/27 [==============================] - 0s 2ms/step - loss: 5.4480e-04 - accuracy: 1.0000 - val_loss: 2.8417 - val_accuracy: 0.7278
Epoch 480/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0071 - accuracy: 0.9988 - val_loss: 2.8767 - val_accuracy: 0.7278
Epoch 481/750
27/27 [==============================] - 0s 2ms/step - loss: 5.1902e-04 - accuracy: 1.0000 - val_loss: 2.9124 - val_accuracy: 0.7295
Epoch 482/750
27/27 [==============================] - 0s 2ms/step - loss: 5.4093e-04 - accuracy: 1.0000 - val_loss: 2.9220 - val_accuracy: 0.7278
Epoch 483/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0052 - accuracy: 0.9988 - val_loss: 2.9493 - val_accuracy: 0.7278
Epoch 484/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9976 - val_loss: 2.8079 - val_accuracy: 0.7313
Epoch 485/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0054 - accuracy: 0.9988 - val_loss: 2.7588 - val_accuracy: 0.7260
Epoch 486/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9988 - val_loss: 2.8767 - val_accuracy: 0.7349
Epoch 487/750
27/27 [==============================] - 0s 2ms/step - loss: 5.1768e-04 - accuracy: 1.0000 - val_loss: 2.8904 - val_accuracy: 0.7313
Epoch 488/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8768 - val_accuracy: 0.7367
Epoch 489/750
27/27 [==============================] - 0s 2ms/step - loss: 4.5027e-04 - accuracy: 1.0000 - val_loss: 2.9048 - val_accuracy: 0.7367
Epoch 490/750
27/27 [==============================] - 0s 2ms/step - loss: 6.5566e-04 - accuracy: 1.0000 - val_loss: 2.9476 - val_accuracy: 0.7367
Epoch 491/750
27/27 [==============================] - 0s 2ms/step - loss: 9.1103e-04 - accuracy: 1.0000 - val_loss: 3.1142 - val_accuracy: 0.7402
Epoch 492/750
27/27 [==============================] - 0s 2ms/step - loss: 9.0982e-04 - accuracy: 1.0000 - val_loss: 3.1184 - val_accuracy: 0.7420
Epoch 493/750
27/27 [==============================] - 0s 2ms/step - loss: 3.5075e-04 - accuracy: 1.0000 - val_loss: 3.1242 - val_accuracy: 0.7384
Epoch 494/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 0.9988 - val_loss: 3.1825 - val_accuracy: 0.7420
Epoch 495/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9988 - val_loss: 3.1851 - val_accuracy: 0.7295
Epoch 496/750
27/27 [==============================] - 0s 2ms/step - loss: 2.9922e-04 - accuracy: 1.0000 - val_loss: 3.2032 - val_accuracy: 0.7278
Epoch 497/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 3.1402 - val_accuracy: 0.7349
Epoch 498/750
27/27 [==============================] - 0s 2ms/step - loss: 2.9670e-04 - accuracy: 1.0000 - val_loss: 3.1452 - val_accuracy: 0.7295
Epoch 499/750
27/27 [==============================] - 0s 2ms/step - loss: 9.2744e-04 - accuracy: 1.0000 - val_loss: 3.2212 - val_accuracy: 0.7278
Epoch 500/750
27/27 [==============================] - 0s 2ms/step - loss: 2.1650e-04 - accuracy: 1.0000 - val_loss: 3.2621 - val_accuracy: 0.7278
Epoch 501/750
27/27 [==============================] - 0s 2ms/step - loss: 7.5441e-04 - accuracy: 1.0000 - val_loss: 3.2924 - val_accuracy: 0.7313
Epoch 502/750
27/27 [==============================] - 0s 2ms/step - loss: 4.7112e-04 - accuracy: 1.0000 - val_loss: 3.2943 - val_accuracy: 0.7295
Epoch 503/750
27/27 [==============================] - 0s 2ms/step - loss: 2.7334e-04 - accuracy: 1.0000 - val_loss: 3.3257 - val_accuracy: 0.7278
Epoch 504/750
27/27 [==============================] - 0s 2ms/step - loss: 9.6338e-04 - accuracy: 1.0000 - val_loss: 3.4437 - val_accuracy: 0.7295
Epoch 505/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1227 - accuracy: 0.9870 - val_loss: 3.4647 - val_accuracy: 0.7100
Epoch 506/750
27/27 [==============================] - 0s 2ms/step - loss: 0.6849 - accuracy: 0.8529 - val_loss: 1.7578 - val_accuracy: 0.6317
Epoch 507/750
27/27 [==============================] - 0s 2ms/step - loss: 0.5149 - accuracy: 0.8790 - val_loss: 1.3107 - val_accuracy: 0.7349
Epoch 508/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1312 - accuracy: 0.9668 - val_loss: 1.5285 - val_accuracy: 0.7456
Epoch 509/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0558 - accuracy: 0.9858 - val_loss: 1.8784 - val_accuracy: 0.7349
Epoch 510/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0650 - accuracy: 0.9834 - val_loss: 1.9990 - val_accuracy: 0.7295
Epoch 511/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0816 - accuracy: 0.9798 - val_loss: 1.9762 - val_accuracy: 0.7278
Epoch 512/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0321 - accuracy: 0.9929 - val_loss: 2.5815 - val_accuracy: 0.7100
Epoch 513/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0329 - accuracy: 0.9941 - val_loss: 2.3716 - val_accuracy: 0.7331
Epoch 514/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0417 - accuracy: 0.9893 - val_loss: 2.6678 - val_accuracy: 0.7242
Epoch 515/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0213 - accuracy: 0.9941 - val_loss: 2.5519 - val_accuracy: 0.7278
Epoch 516/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0935 - accuracy: 0.9763 - val_loss: 2.0074 - val_accuracy: 0.7420
Epoch 517/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0763 - accuracy: 0.9822 - val_loss: 2.0987 - val_accuracy: 0.7367
Epoch 518/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0334 - accuracy: 0.9953 - val_loss: 2.3988 - val_accuracy: 0.7153
Epoch 519/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0454 - accuracy: 0.9893 - val_loss: 2.2744 - val_accuracy: 0.7153
Epoch 520/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0563 - accuracy: 0.9822 - val_loss: 1.6783 - val_accuracy: 0.7687
Epoch 521/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0733 - accuracy: 0.9798 - val_loss: 2.0862 - val_accuracy: 0.7491
Epoch 522/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0367 - accuracy: 0.9893 - val_loss: 2.2574 - val_accuracy: 0.7633
Epoch 523/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 0.9953 - val_loss: 1.9720 - val_accuracy: 0.7651
Epoch 524/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0074 - accuracy: 0.9988 - val_loss: 2.1408 - val_accuracy: 0.7651
Epoch 525/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 2.2524 - val_accuracy: 0.7562
Epoch 526/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.3184 - val_accuracy: 0.7527
Epoch 527/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 2.4034 - val_accuracy: 0.7491
Epoch 528/750
27/27 [==============================] - 0s 2ms/step - loss: 8.3055e-04 - accuracy: 1.0000 - val_loss: 2.4531 - val_accuracy: 0.7544
Epoch 529/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0219 - accuracy: 0.9976 - val_loss: 2.3304 - val_accuracy: 0.7527
Epoch 530/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.3426 - val_accuracy: 0.7598
Epoch 531/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.4192 - val_accuracy: 0.7616
Epoch 532/750
27/27 [==============================] - 0s 2ms/step - loss: 8.9821e-04 - accuracy: 1.0000 - val_loss: 2.4848 - val_accuracy: 0.7580
Epoch 533/750
27/27 [==============================] - 0s 2ms/step - loss: 4.4918e-04 - accuracy: 1.0000 - val_loss: 2.5352 - val_accuracy: 0.7562
Epoch 534/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 2.5932 - val_accuracy: 0.7527
Epoch 535/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.7049 - val_accuracy: 0.7562
Epoch 536/750
27/27 [==============================] - 0s 2ms/step - loss: 3.4181e-04 - accuracy: 1.0000 - val_loss: 2.7317 - val_accuracy: 0.7562
Epoch 537/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0161 - accuracy: 0.9964 - val_loss: 2.5887 - val_accuracy: 0.7687
Epoch 538/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 0.9988 - val_loss: 2.7110 - val_accuracy: 0.7473
Epoch 539/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0244 - accuracy: 0.9929 - val_loss: 2.6536 - val_accuracy: 0.7544
Epoch 540/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 2.6927 - val_accuracy: 0.7509
Epoch 541/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0128 - accuracy: 0.9988 - val_loss: 2.6865 - val_accuracy: 0.7544
Epoch 542/750
27/27 [==============================] - 0s 2ms/step - loss: 7.2742e-04 - accuracy: 1.0000 - val_loss: 2.7028 - val_accuracy: 0.7509
Epoch 543/750
27/27 [==============================] - 0s 2ms/step - loss: 7.9764e-04 - accuracy: 1.0000 - val_loss: 2.7536 - val_accuracy: 0.7544
Epoch 544/750
27/27 [==============================] - 0s 3ms/step - loss: 4.8460e-04 - accuracy: 1.0000 - val_loss: 2.8016 - val_accuracy: 0.7527
Epoch 545/750
27/27 [==============================] - 0s 2ms/step - loss: 3.3598e-04 - accuracy: 1.0000 - val_loss: 2.8590 - val_accuracy: 0.7509
Epoch 546/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9988 - val_loss: 3.3397 - val_accuracy: 0.7367
Epoch 547/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0357 - accuracy: 0.9964 - val_loss: 2.7623 - val_accuracy: 0.7598
Epoch 548/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0108 - accuracy: 0.9964 - val_loss: 2.6253 - val_accuracy: 0.7562
Epoch 549/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0234 - accuracy: 0.9964 - val_loss: 2.7098 - val_accuracy: 0.7260
Epoch 550/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0382 - accuracy: 0.9941 - val_loss: 3.0071 - val_accuracy: 0.7224
Epoch 551/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2084 - accuracy: 0.9549 - val_loss: 2.2595 - val_accuracy: 0.7082
Epoch 552/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1698 - accuracy: 0.9656 - val_loss: 1.8274 - val_accuracy: 0.7260
Epoch 553/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0708 - accuracy: 0.9822 - val_loss: 1.9036 - val_accuracy: 0.7438
Epoch 554/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0702 - accuracy: 0.9858 - val_loss: 1.9988 - val_accuracy: 0.7046
Epoch 555/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0854 - accuracy: 0.9751 - val_loss: 2.0525 - val_accuracy: 0.7295
Epoch 556/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0349 - accuracy: 0.9917 - val_loss: 2.1537 - val_accuracy: 0.7384
Epoch 557/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0117 - accuracy: 0.9976 - val_loss: 2.3012 - val_accuracy: 0.7438
Epoch 558/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.2300 - val_accuracy: 0.7420
Epoch 559/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 2.3107 - val_accuracy: 0.7527
Epoch 560/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 2.5353 - val_accuracy: 0.7456
Epoch 561/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 2.4969 - val_accuracy: 0.7438
Epoch 562/750
27/27 [==============================] - 0s 2ms/step - loss: 9.9969e-04 - accuracy: 1.0000 - val_loss: 2.5027 - val_accuracy: 0.7473
Epoch 563/750
27/27 [==============================] - 0s 2ms/step - loss: 7.9256e-04 - accuracy: 1.0000 - val_loss: 2.5415 - val_accuracy: 0.7473
Epoch 564/750
27/27 [==============================] - 0s 2ms/step - loss: 9.8366e-04 - accuracy: 1.0000 - val_loss: 2.5607 - val_accuracy: 0.7491
Epoch 565/750
27/27 [==============================] - 0s 2ms/step - loss: 9.7878e-04 - accuracy: 1.0000 - val_loss: 2.5680 - val_accuracy: 0.7473
Epoch 566/750
27/27 [==============================] - 0s 3ms/step - loss: 3.7221e-04 - accuracy: 1.0000 - val_loss: 2.6336 - val_accuracy: 0.7509
Epoch 567/750
27/27 [==============================] - 0s 2ms/step - loss: 4.5236e-04 - accuracy: 1.0000 - val_loss: 2.6543 - val_accuracy: 0.7509
Epoch 568/750
27/27 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.6964 - val_accuracy: 0.7509
Epoch 569/750
27/27 [==============================] - 0s 2ms/step - loss: 4.1607e-04 - accuracy: 1.0000 - val_loss: 2.7692 - val_accuracy: 0.7527
Epoch 570/750
27/27 [==============================] - 0s 2ms/step - loss: 3.8522e-04 - accuracy: 1.0000 - val_loss: 2.7806 - val_accuracy: 0.7544
Epoch 571/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0071 - accuracy: 0.9988 - val_loss: 2.7306 - val_accuracy: 0.7544
Epoch 572/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 2.6173 - val_accuracy: 0.7420
Epoch 573/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 0.9988 - val_loss: 2.6973 - val_accuracy: 0.7491
Epoch 574/750
27/27 [==============================] - 0s 2ms/step - loss: 4.6206e-04 - accuracy: 1.0000 - val_loss: 2.7839 - val_accuracy: 0.7527
Epoch 575/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 2.8164 - val_accuracy: 0.7527
Epoch 576/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 2.8249 - val_accuracy: 0.7580
Epoch 577/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.8376 - val_accuracy: 0.7616
Epoch 578/750
27/27 [==============================] - 0s 2ms/step - loss: 8.6646e-04 - accuracy: 1.0000 - val_loss: 2.8366 - val_accuracy: 0.7616
Epoch 579/750
27/27 [==============================] - 0s 2ms/step - loss: 5.4573e-04 - accuracy: 1.0000 - val_loss: 2.8571 - val_accuracy: 0.7616
Epoch 580/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 0.9988 - val_loss: 2.9241 - val_accuracy: 0.7544
Epoch 581/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 2.9572 - val_accuracy: 0.7580
Epoch 582/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.9436 - val_accuracy: 0.7580
Epoch 583/750
27/27 [==============================] - 0s 2ms/step - loss: 4.6557e-04 - accuracy: 1.0000 - val_loss: 2.9781 - val_accuracy: 0.7598
Epoch 584/750
27/27 [==============================] - 0s 2ms/step - loss: 5.0972e-04 - accuracy: 1.0000 - val_loss: 2.9868 - val_accuracy: 0.7580
Epoch 585/750
27/27 [==============================] - 0s 2ms/step - loss: 4.7463e-04 - accuracy: 1.0000 - val_loss: 3.0257 - val_accuracy: 0.7580
Epoch 586/750
27/27 [==============================] - 0s 2ms/step - loss: 1.9919e-04 - accuracy: 1.0000 - val_loss: 3.0488 - val_accuracy: 0.7580
Epoch 587/750
27/27 [==============================] - 0s 2ms/step - loss: 9.4528e-04 - accuracy: 1.0000 - val_loss: 3.0147 - val_accuracy: 0.7580
Epoch 588/750
27/27 [==============================] - 0s 2ms/step - loss: 2.1540e-04 - accuracy: 1.0000 - val_loss: 3.0328 - val_accuracy: 0.7544
Epoch 589/750
27/27 [==============================] - 0s 2ms/step - loss: 2.1663e-04 - accuracy: 1.0000 - val_loss: 3.0401 - val_accuracy: 0.7562
Epoch 590/750
27/27 [==============================] - 0s 2ms/step - loss: 2.2311e-04 - accuracy: 1.0000 - val_loss: 3.0566 - val_accuracy: 0.7580
Epoch 591/750
27/27 [==============================] - 0s 2ms/step - loss: 7.1746e-04 - accuracy: 1.0000 - val_loss: 3.0566 - val_accuracy: 0.7580
Epoch 592/750
27/27 [==============================] - 0s 2ms/step - loss: 2.9370e-04 - accuracy: 1.0000 - val_loss: 3.0732 - val_accuracy: 0.7544
Epoch 593/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2228e-04 - accuracy: 1.0000 - val_loss: 3.1039 - val_accuracy: 0.7509
Epoch 594/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0016e-04 - accuracy: 1.0000 - val_loss: 3.1172 - val_accuracy: 0.7509
Epoch 595/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0001e-04 - accuracy: 1.0000 - val_loss: 3.1427 - val_accuracy: 0.7491
Epoch 596/750
27/27 [==============================] - 0s 2ms/step - loss: 8.1961e-05 - accuracy: 1.0000 - val_loss: 3.1622 - val_accuracy: 0.7473
Epoch 597/750
27/27 [==============================] - 0s 2ms/step - loss: 5.7705e-04 - accuracy: 1.0000 - val_loss: 3.1994 - val_accuracy: 0.7491
Epoch 598/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0096e-04 - accuracy: 1.0000 - val_loss: 3.2237 - val_accuracy: 0.7509
Epoch 599/750
27/27 [==============================] - 0s 2ms/step - loss: 1.8436e-04 - accuracy: 1.0000 - val_loss: 3.2563 - val_accuracy: 0.7491
Epoch 600/750
27/27 [==============================] - 0s 2ms/step - loss: 7.7130e-05 - accuracy: 1.0000 - val_loss: 3.2723 - val_accuracy: 0.7491
Epoch 601/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 0.9988 - val_loss: 3.2585 - val_accuracy: 0.7473
Epoch 602/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0931e-04 - accuracy: 1.0000 - val_loss: 3.3025 - val_accuracy: 0.7509
Epoch 603/750
27/27 [==============================] - 0s 2ms/step - loss: 7.3902e-05 - accuracy: 1.0000 - val_loss: 3.3309 - val_accuracy: 0.7527
Epoch 604/750
27/27 [==============================] - 0s 2ms/step - loss: 1.3499e-04 - accuracy: 1.0000 - val_loss: 3.3663 - val_accuracy: 0.7544
Epoch 605/750
27/27 [==============================] - 0s 2ms/step - loss: 2.8479e-04 - accuracy: 1.0000 - val_loss: 3.3941 - val_accuracy: 0.7562
Epoch 606/750
27/27 [==============================] - 0s 2ms/step - loss: 4.4744e-04 - accuracy: 1.0000 - val_loss: 3.4160 - val_accuracy: 0.7544
Epoch 607/750
27/27 [==============================] - 0s 2ms/step - loss: 1.7857e-04 - accuracy: 1.0000 - val_loss: 3.4026 - val_accuracy: 0.7527
Epoch 608/750
27/27 [==============================] - 0s 2ms/step - loss: 1.1653e-04 - accuracy: 1.0000 - val_loss: 3.6712 - val_accuracy: 0.7438
Epoch 609/750
27/27 [==============================] - 0s 2ms/step - loss: 8.9028e-04 - accuracy: 1.0000 - val_loss: 3.7081 - val_accuracy: 0.7402
Epoch 610/750
27/27 [==============================] - 0s 2ms/step - loss: 1.6847e-04 - accuracy: 1.0000 - val_loss: 3.7128 - val_accuracy: 0.7420
Epoch 611/750
27/27 [==============================] - 0s 2ms/step - loss: 7.6159e-05 - accuracy: 1.0000 - val_loss: 3.8000 - val_accuracy: 0.7598
Epoch 612/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2537e-04 - accuracy: 1.0000 - val_loss: 3.8048 - val_accuracy: 0.7633
Epoch 613/750
27/27 [==============================] - 0s 2ms/step - loss: 1.5084e-04 - accuracy: 1.0000 - val_loss: 3.7526 - val_accuracy: 0.7616
Epoch 614/750
27/27 [==============================] - 0s 2ms/step - loss: 8.4907e-05 - accuracy: 1.0000 - val_loss: 3.7652 - val_accuracy: 0.7633
Epoch 615/750
27/27 [==============================] - 0s 2ms/step - loss: 7.7931e-05 - accuracy: 1.0000 - val_loss: 3.7731 - val_accuracy: 0.7616
Epoch 616/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0494e-04 - accuracy: 1.0000 - val_loss: 3.7557 - val_accuracy: 0.7633
Epoch 617/750
27/27 [==============================] - 0s 2ms/step - loss: 4.2542e-05 - accuracy: 1.0000 - val_loss: 3.7642 - val_accuracy: 0.7651
Epoch 618/750
27/27 [==============================] - 0s 2ms/step - loss: 4.1767e-05 - accuracy: 1.0000 - val_loss: 3.7756 - val_accuracy: 0.7616
Epoch 619/750
27/27 [==============================] - 0s 2ms/step - loss: 2.5256e-04 - accuracy: 1.0000 - val_loss: 3.8063 - val_accuracy: 0.7633
Epoch 620/750
27/27 [==============================] - 0s 2ms/step - loss: 4.3807e-04 - accuracy: 1.0000 - val_loss: 3.8946 - val_accuracy: 0.7527
Epoch 621/750
27/27 [==============================] - 0s 2ms/step - loss: 1.3915e-04 - accuracy: 1.0000 - val_loss: 4.0011 - val_accuracy: 0.7438
Epoch 622/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0801e-04 - accuracy: 1.0000 - val_loss: 4.0031 - val_accuracy: 0.7438
Epoch 623/750
27/27 [==============================] - 0s 2ms/step - loss: 4.4528e-05 - accuracy: 1.0000 - val_loss: 4.0209 - val_accuracy: 0.7473
Epoch 624/750
27/27 [==============================] - 0s 2ms/step - loss: 9.1955e-05 - accuracy: 1.0000 - val_loss: 3.9380 - val_accuracy: 0.7509
Epoch 625/750
27/27 [==============================] - 0s 2ms/step - loss: 4.4252e-04 - accuracy: 1.0000 - val_loss: 3.8808 - val_accuracy: 0.7544
Epoch 626/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0241 - accuracy: 0.9964 - val_loss: 3.8762 - val_accuracy: 0.7491
Epoch 627/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1064 - accuracy: 0.9775 - val_loss: 2.8774 - val_accuracy: 0.7135
Epoch 628/750
27/27 [==============================] - 0s 2ms/step - loss: 0.4220 - accuracy: 0.8980 - val_loss: 1.6709 - val_accuracy: 0.7206
Epoch 629/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2078 - accuracy: 0.9395 - val_loss: 1.9308 - val_accuracy: 0.7153
Epoch 630/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0384 - accuracy: 0.9905 - val_loss: 2.1386 - val_accuracy: 0.7278
Epoch 631/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1174 - accuracy: 0.9775 - val_loss: 1.9405 - val_accuracy: 0.7260
Epoch 632/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0852 - accuracy: 0.9810 - val_loss: 2.0875 - val_accuracy: 0.7082
Epoch 633/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0194 - accuracy: 0.9964 - val_loss: 1.8331 - val_accuracy: 0.7598
Epoch 634/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 2.0458 - val_accuracy: 0.7562
Epoch 635/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 2.1281 - val_accuracy: 0.7598
Epoch 636/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.1867 - val_accuracy: 0.7598
Epoch 637/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.2659 - val_accuracy: 0.7580
Epoch 638/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9988 - val_loss: 2.3057 - val_accuracy: 0.7598
Epoch 639/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 2.3325 - val_accuracy: 0.7616
Epoch 640/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 2.3844 - val_accuracy: 0.7633
Epoch 641/750
27/27 [==============================] - 0s 2ms/step - loss: 9.5624e-04 - accuracy: 1.0000 - val_loss: 2.4332 - val_accuracy: 0.7616
Epoch 642/750
27/27 [==============================] - 0s 2ms/step - loss: 4.4369e-04 - accuracy: 1.0000 - val_loss: 2.4714 - val_accuracy: 0.7616
Epoch 643/750
27/27 [==============================] - 0s 2ms/step - loss: 4.0203e-04 - accuracy: 1.0000 - val_loss: 2.5042 - val_accuracy: 0.7598
Epoch 644/750
27/27 [==============================] - 0s 2ms/step - loss: 9.4873e-04 - accuracy: 1.0000 - val_loss: 2.5398 - val_accuracy: 0.7580
Epoch 645/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.6343 - val_accuracy: 0.7562
Epoch 646/750
27/27 [==============================] - 0s 2ms/step - loss: 4.0136e-04 - accuracy: 1.0000 - val_loss: 2.6885 - val_accuracy: 0.7616
Epoch 647/750
27/27 [==============================] - 0s 2ms/step - loss: 4.4283e-04 - accuracy: 1.0000 - val_loss: 2.7244 - val_accuracy: 0.7598
Epoch 648/750
27/27 [==============================] - 0s 2ms/step - loss: 4.7468e-04 - accuracy: 1.0000 - val_loss: 2.7637 - val_accuracy: 0.7616
Epoch 649/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0054 - accuracy: 0.9976 - val_loss: 2.6782 - val_accuracy: 0.7580
Epoch 650/750
27/27 [==============================] - 0s 2ms/step - loss: 4.3588e-04 - accuracy: 1.0000 - val_loss: 2.6535 - val_accuracy: 0.7527
Epoch 651/750
27/27 [==============================] - 0s 2ms/step - loss: 8.4109e-04 - accuracy: 1.0000 - val_loss: 2.6847 - val_accuracy: 0.7580
Epoch 652/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0127 - accuracy: 0.9988 - val_loss: 2.6245 - val_accuracy: 0.7544
Epoch 653/750
27/27 [==============================] - 0s 2ms/step - loss: 5.8848e-04 - accuracy: 1.0000 - val_loss: 2.6546 - val_accuracy: 0.7473
Epoch 654/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9988 - val_loss: 2.6741 - val_accuracy: 0.7562
Epoch 655/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 2.5156 - val_accuracy: 0.7580
Epoch 656/750
27/27 [==============================] - 0s 2ms/step - loss: 3.2030e-04 - accuracy: 1.0000 - val_loss: 2.5429 - val_accuracy: 0.7562
Epoch 657/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.6216 - val_accuracy: 0.7562
Epoch 658/750
27/27 [==============================] - 0s 2ms/step - loss: 6.5362e-04 - accuracy: 1.0000 - val_loss: 2.7036 - val_accuracy: 0.7527
Epoch 659/750
27/27 [==============================] - 0s 2ms/step - loss: 3.6187e-04 - accuracy: 1.0000 - val_loss: 2.7133 - val_accuracy: 0.7562
Epoch 660/750
27/27 [==============================] - 0s 2ms/step - loss: 5.2855e-04 - accuracy: 1.0000 - val_loss: 2.7513 - val_accuracy: 0.7580
Epoch 661/750
27/27 [==============================] - 0s 2ms/step - loss: 1.7589e-04 - accuracy: 1.0000 - val_loss: 2.7764 - val_accuracy: 0.7580
Epoch 662/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 2.8095 - val_accuracy: 0.7544
Epoch 663/750
27/27 [==============================] - 0s 2ms/step - loss: 1.9480e-04 - accuracy: 1.0000 - val_loss: 2.8392 - val_accuracy: 0.7544
Epoch 664/750
27/27 [==============================] - 0s 2ms/step - loss: 3.4439e-04 - accuracy: 1.0000 - val_loss: 2.8800 - val_accuracy: 0.7527
Epoch 665/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 0.9988 - val_loss: 2.9012 - val_accuracy: 0.7527
Epoch 666/750
27/27 [==============================] - 0s 2ms/step - loss: 9.9800e-04 - accuracy: 1.0000 - val_loss: 2.8792 - val_accuracy: 0.7509
Epoch 667/750
27/27 [==============================] - 0s 2ms/step - loss: 1.3046e-04 - accuracy: 1.0000 - val_loss: 2.8968 - val_accuracy: 0.7544
Epoch 668/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 3.0344 - val_accuracy: 0.7473
Epoch 669/750
27/27 [==============================] - 0s 2ms/step - loss: 1.8974e-04 - accuracy: 1.0000 - val_loss: 3.0669 - val_accuracy: 0.7456
Epoch 670/750
27/27 [==============================] - 0s 2ms/step - loss: 3.1324e-04 - accuracy: 1.0000 - val_loss: 3.0891 - val_accuracy: 0.7473
Epoch 671/750
27/27 [==============================] - 0s 2ms/step - loss: 6.2708e-04 - accuracy: 1.0000 - val_loss: 3.1065 - val_accuracy: 0.7473
Epoch 672/750
27/27 [==============================] - 0s 2ms/step - loss: 1.8693e-04 - accuracy: 1.0000 - val_loss: 3.1538 - val_accuracy: 0.7456
Epoch 673/750
27/27 [==============================] - 0s 2ms/step - loss: 1.2799e-04 - accuracy: 1.0000 - val_loss: 3.1543 - val_accuracy: 0.7473
Epoch 674/750
27/27 [==============================] - 0s 2ms/step - loss: 2.3970e-04 - accuracy: 1.0000 - val_loss: 3.1552 - val_accuracy: 0.7438
Epoch 675/750
27/27 [==============================] - 0s 2ms/step - loss: 7.8064e-04 - accuracy: 1.0000 - val_loss: 3.1736 - val_accuracy: 0.7527
Epoch 676/750
27/27 [==============================] - 0s 2ms/step - loss: 7.0209e-04 - accuracy: 1.0000 - val_loss: 3.1884 - val_accuracy: 0.7527
Epoch 677/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0179e-04 - accuracy: 1.0000 - val_loss: 3.1968 - val_accuracy: 0.7544
Epoch 678/750
27/27 [==============================] - 0s 2ms/step - loss: 9.6339e-05 - accuracy: 1.0000 - val_loss: 3.2006 - val_accuracy: 0.7544
Epoch 679/750
27/27 [==============================] - 0s 2ms/step - loss: 9.6900e-05 - accuracy: 1.0000 - val_loss: 3.2094 - val_accuracy: 0.7544
Epoch 680/750
27/27 [==============================] - 0s 2ms/step - loss: 1.0489e-04 - accuracy: 1.0000 - val_loss: 3.2160 - val_accuracy: 0.7544
Epoch 681/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0049 - accuracy: 0.9976 - val_loss: 3.1166 - val_accuracy: 0.7616
Epoch 682/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.8825 - val_accuracy: 0.7740
Epoch 683/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1188 - accuracy: 0.9822 - val_loss: 2.5272 - val_accuracy: 0.7331
Epoch 684/750
27/27 [==============================] - 0s 2ms/step - loss: 0.4386 - accuracy: 0.9063 - val_loss: 1.4794 - val_accuracy: 0.6779
Epoch 685/750
27/27 [==============================] - 0s 2ms/step - loss: 0.2475 - accuracy: 0.9265 - val_loss: 1.9435 - val_accuracy: 0.6975
Epoch 686/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0789 - accuracy: 0.9822 - val_loss: 1.9065 - val_accuracy: 0.7313
Epoch 687/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0175 - accuracy: 0.9976 - val_loss: 2.2665 - val_accuracy: 0.7295
Epoch 688/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0157 - accuracy: 0.9964 - val_loss: 2.3646 - val_accuracy: 0.7295
Epoch 689/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0125 - accuracy: 0.9988 - val_loss: 2.6752 - val_accuracy: 0.7189
Epoch 690/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 2.5681 - val_accuracy: 0.7402
Epoch 691/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0178 - accuracy: 0.9976 - val_loss: 2.6059 - val_accuracy: 0.7295
Epoch 692/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0989 - accuracy: 0.9798 - val_loss: 2.1838 - val_accuracy: 0.7331
Epoch 693/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0583 - accuracy: 0.9917 - val_loss: 2.2911 - val_accuracy: 0.7171
Epoch 694/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0455 - accuracy: 0.9905 - val_loss: 2.3563 - val_accuracy: 0.7189
Epoch 695/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1137 - accuracy: 0.9739 - val_loss: 2.5632 - val_accuracy: 0.7011
Epoch 696/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0389 - accuracy: 0.9893 - val_loss: 1.9807 - val_accuracy: 0.7367
Epoch 697/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0398 - accuracy: 0.9929 - val_loss: 2.3263 - val_accuracy: 0.7295
Epoch 698/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0160 - accuracy: 0.9953 - val_loss: 2.2073 - val_accuracy: 0.7456
Epoch 699/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0215 - accuracy: 0.9953 - val_loss: 2.5016 - val_accuracy: 0.7331
Epoch 700/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0216 - accuracy: 0.9953 - val_loss: 2.6409 - val_accuracy: 0.7117
Epoch 701/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0221 - accuracy: 0.9941 - val_loss: 2.6838 - val_accuracy: 0.7278
Epoch 702/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0245 - accuracy: 0.9964 - val_loss: 2.5718 - val_accuracy: 0.7295
Epoch 703/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0277 - accuracy: 0.9929 - val_loss: 2.6175 - val_accuracy: 0.7331
Epoch 704/750
27/27 [==============================] - 0s 2ms/step - loss: 0.1085 - accuracy: 0.9786 - val_loss: 2.2943 - val_accuracy: 0.7509
Epoch 705/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0938 - accuracy: 0.9786 - val_loss: 2.6003 - val_accuracy: 0.7295
Epoch 706/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0192 - accuracy: 0.9964 - val_loss: 2.4311 - val_accuracy: 0.7456
Epoch 707/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0142 - accuracy: 0.9964 - val_loss: 2.6240 - val_accuracy: 0.7242
Epoch 708/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0887 - accuracy: 0.9846 - val_loss: 2.4275 - val_accuracy: 0.7028
Epoch 709/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0675 - accuracy: 0.9870 - val_loss: 2.4290 - val_accuracy: 0.7224
Epoch 710/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0510 - accuracy: 0.9929 - val_loss: 2.2570 - val_accuracy: 0.7313
Epoch 711/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 2.3275 - val_accuracy: 0.7456
Epoch 712/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 2.3492 - val_accuracy: 0.7509
Epoch 713/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 2.4092 - val_accuracy: 0.7473
Epoch 714/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.4686 - val_accuracy: 0.7473
Epoch 715/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.5274 - val_accuracy: 0.7438
Epoch 716/750
27/27 [==============================] - 0s 2ms/step - loss: 6.8498e-04 - accuracy: 1.0000 - val_loss: 2.6096 - val_accuracy: 0.7402
Epoch 717/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.6696 - val_accuracy: 0.7402
Epoch 718/750
27/27 [==============================] - 0s 2ms/step - loss: 7.5848e-04 - accuracy: 1.0000 - val_loss: 2.6625 - val_accuracy: 0.7420
Epoch 719/750
27/27 [==============================] - 0s 2ms/step - loss: 4.2598e-04 - accuracy: 1.0000 - val_loss: 2.6724 - val_accuracy: 0.7420
Epoch 720/750
27/27 [==============================] - 0s 2ms/step - loss: 4.0998e-04 - accuracy: 1.0000 - val_loss: 2.6968 - val_accuracy: 0.7420
Epoch 721/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 2.7634 - val_accuracy: 0.7438
Epoch 722/750
27/27 [==============================] - 0s 2ms/step - loss: 7.2751e-04 - accuracy: 1.0000 - val_loss: 2.8616 - val_accuracy: 0.7420
Epoch 723/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9976 - val_loss: 2.9547 - val_accuracy: 0.7349
Epoch 724/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 3.0088 - val_accuracy: 0.7331
Epoch 725/750
27/27 [==============================] - 0s 2ms/step - loss: 3.3433e-04 - accuracy: 1.0000 - val_loss: 2.9984 - val_accuracy: 0.7331
Epoch 726/750
27/27 [==============================] - 0s 2ms/step - loss: 4.6574e-04 - accuracy: 1.0000 - val_loss: 2.9983 - val_accuracy: 0.7349
Epoch 727/750
27/27 [==============================] - 0s 2ms/step - loss: 2.0618e-04 - accuracy: 1.0000 - val_loss: 3.0129 - val_accuracy: 0.7349
Epoch 728/750
27/27 [==============================] - 0s 2ms/step - loss: 2.2255e-04 - accuracy: 1.0000 - val_loss: 3.0287 - val_accuracy: 0.7367
Epoch 729/750
27/27 [==============================] - 0s 2ms/step - loss: 2.8452e-04 - accuracy: 1.0000 - val_loss: 3.0532 - val_accuracy: 0.7384
Epoch 730/750
27/27 [==============================] - 0s 2ms/step - loss: 1.5129e-04 - accuracy: 1.0000 - val_loss: 3.0747 - val_accuracy: 0.7367
Epoch 731/750
27/27 [==============================] - 0s 2ms/step - loss: 2.1172e-04 - accuracy: 1.0000 - val_loss: 3.0876 - val_accuracy: 0.7367
Epoch 732/750
27/27 [==============================] - 0s 2ms/step - loss: 3.0242e-04 - accuracy: 1.0000 - val_loss: 3.1009 - val_accuracy: 0.7420
Epoch 733/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 0.9988 - val_loss: 3.1036 - val_accuracy: 0.7367
Epoch 734/750
27/27 [==============================] - 0s 2ms/step - loss: 2.9435e-04 - accuracy: 1.0000 - val_loss: 3.1035 - val_accuracy: 0.7402
Epoch 735/750
27/27 [==============================] - 0s 2ms/step - loss: 3.3230e-04 - accuracy: 1.0000 - val_loss: 3.1165 - val_accuracy: 0.7402
Epoch 736/750
27/27 [==============================] - 0s 2ms/step - loss: 5.3612e-04 - accuracy: 1.0000 - val_loss: 3.1414 - val_accuracy: 0.7402
Epoch 737/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9976 - val_loss: 3.2017 - val_accuracy: 0.7402
Epoch 738/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 0.9988 - val_loss: 3.1047 - val_accuracy: 0.7384
Epoch 739/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0252 - accuracy: 0.9953 - val_loss: 3.3888 - val_accuracy: 0.7242
Epoch 740/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0546 - accuracy: 0.9881 - val_loss: 2.7365 - val_accuracy: 0.7295
Epoch 741/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0211 - accuracy: 0.9929 - val_loss: 2.5424 - val_accuracy: 0.7367
Epoch 742/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 2.8026 - val_accuracy: 0.7456
Epoch 743/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.8939 - val_accuracy: 0.7473
Epoch 744/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9988 - val_loss: 2.8224 - val_accuracy: 0.7473
Epoch 745/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.8255 - val_accuracy: 0.7598
Epoch 746/750
27/27 [==============================] - 0s 2ms/step - loss: 9.5804e-04 - accuracy: 1.0000 - val_loss: 2.8954 - val_accuracy: 0.7544
Epoch 747/750
27/27 [==============================] - 0s 2ms/step - loss: 3.0087e-04 - accuracy: 1.0000 - val_loss: 2.9369 - val_accuracy: 0.7544
Epoch 748/750
27/27 [==============================] - 0s 2ms/step - loss: 3.1512e-04 - accuracy: 1.0000 - val_loss: 2.9854 - val_accuracy: 0.7527
Epoch 749/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9988 - val_loss: 2.9870 - val_accuracy: 0.7527
Epoch 750/750
27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 0.9988 - val_loss: 2.9921 - val_accuracy: 0.7491
In [27]:
model.save("songs_model.keras")
In [28]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)
In [29]:
plt.plot(epochs, acc)
plt.plot(epochs, val_acc)
plt.legend(["Accuracy", "Val_Accuracy"])
plt.show()
In [30]:
Y_pred = model.predict(X_test)
y_pred = np.argmax(Y_pred, axis = 1)

18/18 [==============================] - 0s 784us/step
In [31]:
r2_score(Y_test, y_pred)
Out[31]:
0.4615727999243392
In [32]:
correct = 0
total = 0
for i in range(0, len(y_pred)):
  if Y_test[i] == y_pred[i]:
    correct += 1
  total += 1
round(correct/total, 4) * 100
Out[32]:
74.91
In [33]:
cm = confusion_matrix(Y_test, y_pred)
for i in range(0, len(cm[0])):
  cm[i] = cm[i] * 100 / float(sum(cm[i]))

cm
Out[33]:
array([[76,  7,  0,  2,  0,  2,  7,  0,  2],
       [ 1, 81, 10,  1,  0,  0,  1,  3,  1],
       [ 1, 14, 73,  3,  0,  1,  2,  0,  4],
       [ 4,  0, 12, 46,  0, 21,  0,  7,  7],
       [ 2,  2, 11,  0, 76,  0,  0,  5,  0],
       [ 0,  0,  8,  8,  0, 56,  4, 17,  4],
       [ 0,  1,  2,  0,  0,  0, 93,  0,  0],
       [ 1,  2,  8,  4,  1,  1,  0, 70,  9],
       [ 4, 16,  4,  2,  0,  6, 14,  0, 54]])
In [34]:
ar = []
for i in range(0, len(np.unique(Y_t))):
    ar.append(i)
dl = l.inverse_transform(ar)
dl
Out[34]:
array(['ABBA', 'Beach Boys', 'Beatles', 'Bob Dylan', 'Elvis Presley',
       'Led Zeppelin', 'Nirvana', 'Pink Floyd', 'Queen'], dtype='<U13')
In [35]:
cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = np.char.lower(dl))
cm_display.plot()
plt.title("Confusion Matrix")
plt.xticks(rotation=90)
plt.show()
In [36]:
def prediction(filename):
    mfcc = features_extractor(filename)
    time = duration(filename)
    mfcc = np.append(mfcc, time)
    pred = model.predict(np.array([mfcc]))[0]
    arr = np.argsort(-pred)
    arr = arr[:5]
    index = arr[0]
    y_t = pred[arr] * 100
    arr = l.inverse_transform(arr)
    na = l.inverse_transform([index])[0]
    file = "Pics/" + na + ".jpg"
  #  songs = os.listdir("Training_Files/" + na)
  #  r = random.randint(0, len(songs) - 1)
  #  fname = "Training_Files/" + na + "/" + songs[r]
  #  soundna = ipd.Audio(fname)
  #  soundfn = ipd.Audio(filename)
    plt.imshow(cv2.imread(file))
    plt.show()
  #  print(na + " Example")
  #  display(soundna)
  #  print("Original Song")
  #  display(soundfn)
    D = dict(zip(np.char.upper(arr), y_t))
    return D
In [37]:
prediction("Training_Files/Beatles/SheLovesYouRemastered2009.wav")

1/1 [==============================] - 0s 14ms/step
Out[37]:
{'BEATLES': 100.0,
 'BEACH BOYS': 6.4731546e-07,
 'ELVIS PRESLEY': 1.0829525e-09,
 'PINK FLOYD': 7.123964e-10,
 'NIRVANA': 4.5190177e-10}
In [38]:
prediction("Training_Files/Nirvana/DrainYou.wav")

1/1 [==============================] - 0s 13ms/step
Out[38]:
{'NIRVANA': 100.0,
 'LED ZEPPELIN': 1.8606692e-15,
 'QUEEN': 9.907608e-16,
 'ABBA': 3.4589536e-22,
 'BEATLES': 6.5398825e-26}
In [39]:
prediction("Linda/YoureNoGood.wav")

1/1 [==============================] - 0s 12ms/step
Out[39]:
{'BEACH BOYS': 98.36874,
 'PINK FLOYD': 0.90323025,
 'QUEEN': 0.5444898,
 'BEATLES': 0.17494287,
 'NIRVANA': 0.008595253}
In [40]:
prediction("Linda/BlueBayou.wav")

1/1 [==============================] - 0s 13ms/step
Out[40]:
{'PINK FLOYD': 97.80583,
 'BOB DYLAN': 1.5199271,
 'BEATLES': 0.40621528,
 'LED ZEPPELIN': 0.112754375,
 'BEACH BOYS': 0.089594826}
In [41]:
prediction("Linda/ThatllBetheDay.wav")

1/1 [==============================] - 0s 13ms/step
Out[41]:
{'PINK FLOYD': 50.24096,
 'QUEEN': 36.67983,
 'BEATLES': 6.1744504,
 'BOB DYLAN': 1.8168545,
 'ABBA': 1.7107702}
In [42]:
prediction("Olivia/vampire.wav")

1/1 [==============================] - 0s 14ms/step
Out[42]:
{'LED ZEPPELIN': 97.86497,
 'QUEEN': 0.89748913,
 'NIRVANA': 0.7243379,
 'BOB DYLAN': 0.24101114,
 'PINK FLOYD': 0.18197283}
In [43]:
prediction("Olivia/happier.wav")

1/1 [==============================] - 0s 13ms/step
Out[43]:
{'BEACH BOYS': 75.98029,
 'BEATLES': 22.77097,
 'PINK FLOYD': 0.95106333,
 'NIRVANA': 0.1913342,
 'QUEEN': 0.10547596}
In [44]:
prediction("Olivia/badidearight.wav")

1/1 [==============================] - 0s 13ms/step
Out[44]:
{'NIRVANA': 97.52778,
 'LED ZEPPELIN': 1.3726364,
 'QUEEN': 0.88557667,
 'ABBA': 0.11487187,
 'BEATLES': 0.06337819}
In [45]:
prediction("Lana/SummertimeSadness.wav")

1/1 [==============================] - 0s 13ms/step
Out[45]:
{'PINK FLOYD': 97.71829,
 'BEATLES': 0.8376042,
 'BEACH BOYS': 0.52598935,
 'QUEEN': 0.37064466,
 'LED ZEPPELIN': 0.27663594}
In [46]:
prediction("Lana/DietMountainDew.wav")

1/1 [==============================] - 0s 13ms/step
Out[46]:
{'ABBA': 91.74855,
 'BOB DYLAN': 3.9423914,
 'LED ZEPPELIN': 3.0816143,
 'QUEEN': 0.84092003,
 'PINK FLOYD': 0.21689452}
In [47]:
prediction("Lana/OffToTheRaces.wav")

1/1 [==============================] - 0s 13ms/step
Out[47]:
{'ABBA': 99.99597,
 'BOB DYLAN': 0.0028721762,
 'LED ZEPPELIN': 0.0007155619,
 'QUEEN': 0.0004398899,
 'PINK FLOYD': 3.2195221e-06}
In [48]:
prediction("Trial_Songs/Nessa Barrett - die first (official lyric video).wav")

1/1 [==============================] - 0s 13ms/step
Out[48]:
{'NIRVANA': 99.99523,
 'LED ZEPPELIN': 0.004050183,
 'QUEEN': 0.0006745665,
 'ABBA': 3.9137292e-05,
 'BEATLES': 4.566028e-07}
In [49]:
prediction("Trial_Songs/The Neighbourhood - Sweater Weather (Official Video).wav")

1/1 [==============================] - 0s 13ms/step
Out[49]:
{'NIRVANA': 99.437546,
 'LED ZEPPELIN': 0.4332466,
 'QUEEN': 0.10312057,
 'ABBA': 0.022964295,
 'BOB DYLAN': 0.0014360381}
In [50]:
prediction("Trial_Songs/505.wav")

1/1 [==============================] - 0s 13ms/step
Out[50]:
{'PINK FLOYD': 79.67384,
 'BEATLES': 7.5573206,
 'QUEEN': 3.74495,
 'LED ZEPPELIN': 3.1211355,
 'BEACH BOYS': 2.8624616}
In [51]:
prediction("Trial_Songs/Dua Lipa - Levitating Featuring DaBaby (Official Music Video).wav")

1/1 [==============================] - 0s 14ms/step
Out[51]:
{'ABBA': 99.99993,
 'BOB DYLAN': 6.525918e-05,
 'LED ZEPPELIN': 6.739596e-06,
 'QUEEN': 2.1464512e-06,
 'PINK FLOYD': 3.866854e-09}